--- title: "Using the LifeInsureR Package" author: - name: Reinhold Kainhofer affiliation: Open Tools email: reinhold@kainhofer.com date: "`r Sys.Date()`" output: rmarkdown::html_vignette: toc: true toc_depth: 3 fig_width: 7 fig_height: 5 number_sections: true vignette: > %\VignetteIndexEntry{Using the LifeInsureR Package} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r setup, echo = FALSE, message=FALSE} knitr::opts_chunk$set(collapse = TRUE, comment = "#>") library(knitr) library(kableExtra) library(LifeInsureR) library(dplyr) library(tibble) library(lubridate) library(pander) panderOptions('round', 2) panderOptions('digits', 12) panderOptions('keep.trailing.zeros', TRUE) panderOptions('table.split.table', 120) kableTable = function(grd, ...) { grd %>% kable(...) %>% add_header_above(header = c(1, dim(grd)[[2]]) %>% `names<-`(names(dimnames(grd))), align = "c") %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), full_width = F) %>% column_spec(1, bold = T, border_right = T) } ``` The LifeInsureR package provides a full-featured framework to model classical life insurance contracts (non-unit linked). Mathematically, a general life insurance contracts can be defined using death and survival (and disability) benefit vectors to define the cash flows and calculate all premiums and reserves recursively. This powerful approach is taken by the LifeInsureR package to provide the most flexible contract modelling framework in R. # General Overview of the Concepts An insurance contract is described by three different objects; * `InsuranceContract`: The object describing the actual contract with all contract-specific parameters (age, maturity, sum insured, etc.). * `InsuranceTarif`: The general (contract-independent) description of the insurance product (benefits, present values / reserves, premium calculation, premium waivers, surrender value, reserve calculation, premium decomposition). It does not store any values (only tarif-provided default values for the insurance contract), but provides all calculation functions. The implementation is based on a general approach using cash flows (depending on the type of insurance). Once the cash flows (survival, death and guaranteed) are defined for all time steps, all further calculations are idential for all different kinds of life insurance. * ` ProfitParticipation`: For contracts with profit sharing mechanisms, this object describes the profit participation (like the `InsuranceTarif` object describes the guaranteed payments) and us used by the `InsuranceContract` object. The tariff and the profit scheme are purely descriptional and their creation does not trigger any calculations. However, when one creates a contract for a given tariff, the following steps are done to calculate all values relevant for the contract: * Extract the transition probabilities (mortality) * Set up all cash flow time series (premiums, guaranteed payments, survival payments, death payments, disease payments, charged cost cash flows) * Calculate all present values for each time for all the cash flows (benefits, premiums and costs) * Use the actuarial equivalence principle and the present values at time $t=0$ to calculate the premium of the contract * Express all cash flows also in absolute (monetary) terms * Calculate the reserves at every moment * Calculate the premium decomposition * Apply the (optional) profit participation scheme with the given profit scenario * Optionally apply contract changes (premiums waivers, dynamic) and further profit scenarios All steps after the cash flow setup are implemented in a very generic way, as the cash flows fully determine an insurance contract and as soon as the cash flows are fixed, the valuation and reserve calculation can be expressed in terms of expected present values of the cash flows only. While this might at first seem a bit complicated, it allows for very generic implementations of life insurance products and contracts. # A simple example: Term life insurance To understand how the package implements life insurance contracts, let us look at a simple example: ## Product description Term Life Insurance * Public product name '**DeathPlus - Short Term Life Insurance**', Internal ID '**L71-U**' * Only **death benefits**, no survival benefits * **Mortality table**: Austrian 2010/12 census table unisex (mixed 65:35 from the male:female tables) * **Guaranteed interest rate**: 0.5% * Default contract duration: 5 years (to keep the example short) * **Regular premium** payments (constant premiums) during the whole contract **Costs**: * Aquisition costs: 5\% of the total premium sum * Administration cost: 1% of the sum insured per year (both during premium payments as well as for paid-up contracts) * Unit costs: 10 Euro per year (fixed) during premium payments * Tax: 4% insurance tax (default) Surrender Value: * Reserve minus 10% surrender penalty, also applied on premium waiver ## Tariff implementation (InsuranceTarif) ```{r SimpleExampleRiskTarif, warning=F, results="hide", message = F} library(magrittr) library(MortalityTables) library(LifeInsureR) mortalityTables.load("Austria_Census") Tarif.L71U = InsuranceTarif$new( name = "L71-U", type = "wholelife", tarif = "DeathPlus - Short Term Life Insurance", desc = "Term Life insurance (5 years) with constant sum insured and regular premiums", policyPeriod = 5, premiumPeriod = 5, # premiumPeriod not needed, defaults to maturity mortalityTable = mortalityTable.mixed( table1 = mort.AT.census.2011.male, weight1 = 0.65, table2 = mort.AT.census.2011.female, weight2 = 0.35 ), i = 0.005, tax = 0.04, costs = initializeCosts(alpha = 0.05, gamma = 0.01, gamma.paidUp = 0.01, unitcosts = 10), surrenderValueCalculation = function(surrenderReserve, params, values) { surrenderReserve * 0.9 } ); ``` ## Creating a contract With the above product / tariff definition, it is now easy to instantiate one particular contract for this tariff. All we need to do is pass the tariff and the corresponding contract-specific information (mainly age, sum insured and contract closing) to the `InsuranceContract` object: ```{r SimpleExampleRiskContract} contract.L71U = InsuranceContract$new( Tarif.L71U, age = 35, contractClosing = as.Date("2020-08-18"), sumInsured = 100000); ``` Just creating the contract will already calculate all cash flows, present values, premiums, reserves, etc. for the whole contract period. They can be accessed through the `contract.L71U$Values` list. A full list of all possible arguments can be found in the section [All possible parameters and their default values]. Once the contract is created, all values can be accessed like this: ```{r SimpleExampleRiskValuesPremCode, eval=F} contract.L71U$Values$premiums ``` ```{r SimpleExampleRiskValuesPremCodeOut, echo=F} contract.L71U$Values$premiums %>% kable ``` ```{r SimpleExampleRiskValuesResCode, eval=F} contract.L71U$Values$reserves ``` ```{r SimpleExampleRiskValuesResOut, echo=F} contract.L71U$Values$reserves %>% pander() ``` Looking back at the definition of the tariff, the only spot where the type of insurance actually came in was the `type` argument of the `InsuranceTarif` definition. This is one of the most important flags and is central to correct implementation of a tarif. On the other hand, all it does is to cause different vectors of survival, death and guaranteed cash flows. Once the cash flows are determined, the insurance contract and tariff does not need the insurance type any more. In our term life example, the insurance contract's unit cash flows are 1 for death benefits (both when premiums are paid and when the contract is paid-up) and for premiums in advance. All other cash flows (guaranteed, survival or disease cash flows) are zero. Similarly, the cost structure described above and implemented by the `LifeInsureR::initializeCosts()` function defines all cost cash flows, which are the starting point for all further calculations (only relevant columns of the data.frame are shown): ```{r SimpleExampleRiskCFCode, eval=F} contract.L71U$Values$cashFlows ``` ```{r SimpleExampleRiskCFOut, echo=F} contract.L71U$Values$cashFlows %>% select(starts_with('premiums'), starts_with('death'), -death_Refund_past ) %>% pander() ``` ```{r SimpleExampleRiskCFCostCode, eval=F} contract.L71U$Values$cashFlowsCosts[,,,"survival"] ``` ```{r SimpleExampleRiskCFCostOut, echo=F, results="asis"} for (base in dimnames(contract.L71U$Values$cashFlowsCosts)[[3]]) { cat("* ,,\"", base, "\"\n", sep = "") cat(contract.L71U$Values$cashFlowsCosts[,,base, "survival"] %>% pander(round = 4, style = "rmarkdown")) } ``` Once these unit cash flows are set, all insurance contracts are handled identically. First, present values of each of the cash flows are calculated, from which the premiums can be calculated by the equivalence principle. ```{r SimpleExampleRiskPVCode, eval=F} contract.L71U$Values$presentValues ``` ```{r SimpleExampleRiskPVOut, echo=F} contract.L71U$Values$presentValues %>% as.data.frame() %>% select(starts_with('premiums'), starts_with('death'), -death_Refund_past ) %>% pander(round=5) ``` ```{r SimpleExampleRiskPVCostCode, eval=F} contract.L71U$Values$presentValuesCosts ``` ```{r SimpleExampleRiskPVCostOut, echo=F, results="asis"} for (base in dimnames(contract.L71U$Values$presentValuesCosts)[[3]]) { cat("* ,,\"", base, "\"\n", sep = "") cat(contract.L71U$Values$presentValuesCosts[,,base,"survival" ] %>% pander(round = 4, style = "rmarkdown")) } ``` ```{r SimpleExampleRiskPVPremCode, eval=F} contract.L71U$Values$premiums ``` ```{r SimpleExampleRiskPVPremOut, echo=F} contract.L71U$Values$premiums %>% data.frame() %>% pander() ``` The actual calculation of the premiums has to be in the order gross premium, Zillmer premiuem, then net premium. The reason for this particular order is that some tariffs have a gross premium refund in case of death. So to calculate the net premium, the gross premium is required. The premiums allow the unit cash flows and present values to be converted to monetary terms (fields `contract.L71U$Values$absCashFlows` and `contract.L71U$Values$absPresentValues`). Also, the reserves of the contract can be calculated. ```{r SimpleExampleRiskPremiumsCode, eval=F} contract.L71U$Values$reserves ``` ```{r SimpleExampleRiskPremiumsOut, echo=F} contract.L71U$Values$reserves %>% pander(digits=2) ``` Also, the premium composition into costs, risk premium, savings premium and other components is possible. ```{r SimpleExampleRiskPremiumCompositionCode, eval=F} contract.L71U$Values$premiumComposition ``` ```{r SimpleExampleRiskPremiumCompositionOut, echo=F} contract.L71U$Values$premiumComposition %>% as.data.frame() %>% select(-loading.frequency, -rebate.premium, -rebate.partner, -profit.advance, -rebate.sum, -charge.noMedicalExam, -premium.risk.actual, -premium.risk.security, -risk.disease, -premium.risk.disease.actual, -premium.risk.disease.security, -starts_with('Zillmer')) %>% pander() ``` As we see, the whole history and future of the contract is calculated as soon as it is created. It is, however, also possible to modify a contract later on, e.g. by stopping premium payments and converting it to a paid-up contract. ```{r SimpleExampleRiskConversionCode, eval=F} contract.L71U.prf = contract.L71U$premiumWaiver(t = 3) contract.L71U.prf$Values$reserves ``` ```{r SimpleExampleRiskConversionOut, echo=F} contract.L71U.prf = contract.L71U$premiumWaiver(t = 3) contract.L71U.prf$Values$reserves %>% pander() ``` ## Creating tables with various parameters When prototyping a new product or creating marketing material, it is often needed to create tables of premiums, reserves, benefits or surrender values for different parameters (e.g. different ages, maturities and sums insured for the marketing department, or different guaranteed interest rates, mortality tables or costs for the product development group). This can be easily done by the functions `contractGridPremium()` or `contractGrid()`. They take one argument `axes`, which gives the parameters for the axes of the table (more than two dimensions are possible!), while all other parameters are passed unchanged to `InsuranceContract$new()`. First, let us create a grid of premiums for different ages and maturities (for sum insured 100,000 Euro): ```{r SimpleExampleRiskPremiumGrid, eval=T, results="hide"} grd = contractGridPremium( axes = list(age = seq(20, 80, 5), policyPeriod = seq(10, 40, 5)), tarif = Tarif.L71U, contractClosing = as.Date("2020-08-18"), sumInsured = 100000 ) grd ``` ```{r SimpleExampleRiskPremiumGridOut, echo = F} grd %>% kableTable(digits = 2) ``` One can also pass more than two dimensions to the axes: ```{r SimpleExampleRiskPremiumGrid3D, results = "hide"} grd = contractGridPremium( axes = list(age = seq(20, 80, 10), policyPeriod = seq(10, 40, 10), sumInsured = c(10000, 50000, 100000)), tarif = Tarif.L71U, contractClosing = as.Date("2020-08-18") ) grd ``` ```{r SimpleExampleRiskPremiumGrid3DOut, echo=F, results="asis"} for (d in dimnames(grd)[[3]]) { cat("\n", "* , , ", names(dimnames(grd))[[3]], "=", d, "\n\n", sep = "") # cat(grd[,,d ] %>% as.data.frame() %>% rownames_to_column("age \\| policyPeriod") %>% pander(digits = 7, round = 2, style = "rmarkdown")) cat(grd[,,d ] %>% kableTable(digits = 2), "\n") } ``` One can use any of the parameters of the `InsuranceContract$new()` call in the `axes` argument, even the `tarif` or `mortalityTable` parameters. This means that one can create tables comparing different tariffs, or showing the effect of different life tables. In the following example, we use the tarif `Tarif.L71U`, but instead of the unisex table (mixed 65:35 from male:female tables), we use the male mortality tables of the Austrian census from 1870 to 2011 (with a contract period of 10 years fixed, and varying ages): ```{r SimpleExampleRiskPremiumGridLifeTables, results = "hide"} grd = contractGridPremium( axes = list(mortalityTable = mort.AT.census["m", ], age = seq(20, 80, 10)), tarif = Tarif.L71U, sumInsured = 100000, contractClosing = as.Date("2020-08-18") ) grd ``` ```{r SimpleExampleRiskPremiumGridLifeTablesOUT, echo = F} grd %>% pander(round=1, digits=15, keep.trailing.zeros = T) ``` # All possible parameters All possible parameters of an insurance contract are stored in sub-lists of a a structure `InsuranceContract.Parameters`. If not provided by the call to `InsuranceContract$new()`, the values will be taken from either the `InsuranceTariff`'s default parameters, the `ProfitParticipation`'s default parameters or the global defaults in the `InsuranceContract.ParameterDefault`. The cascade or parameters is (from top to bottom): * explicit parameters passed to `InsuranceContract$addProfitScenario()` (applies only for the added profit scenario) * explicit parameters passed to `InsuranceContract$new()` or `InsuranceContract$clone()` * parameters set with the `ProfitParticipation` * parameters set with the `InsuranceTarif` * Default values set in `InsuranceContract.ParameterDefaults` In addition to the parameters listed below, the `InsuranceContract$new()` constructor function takes the following parameters \define{ \item{tarif}{The `InsuranceTarif` object describing the tarif} \item{parent}{For contracts with multiple parts, children get passed a pointer to the parent} \item{calculate}{How much of the contract to calculate (by default everything will be calculated)} \item{profitid}{The profit ID for contracts with profit participation} } ```{r} str(InsuranceContract.ParameterDefaults) ``` ```{r, results="asis"} # pandoc.listRK(InsuranceContract.ParameterDefaults) ``` # Tarif and Contract Specification An insurance contract is modelled by the abstract product specification (`InsuranceTarif` class) and the concrete (individualized) `InsuranceContract`. * The `InsuranceTarif` object describes the product in abstract terms, holds default parameters and provides all calculation functions for cash flows, present values, premiums and reserves (provided the contract's actual Parameters). It does not, however, hold contract-specific data. * The `InsuranceContract` object holds the individual contract data (like age, contract closing date, sum insured, etc.) that override the tariff's defaults. It also holds a pointer to the insurance tariff and provides the general logic to calculate all parts of an insurance contract by calling the corresponding functions of the tariff. The insurance contract and the underlying insurance tariff have the same possible parameters in its constructors: The `InsuranceTarif` uses them to define defaults for contracts that use the tariff, while the parameters passed to the contract either provide the individually required data like age, sum insured or maturity, or they can override the defaults provided by the tariff. In theory, one could even create a contract with an empty underlying tariff and provide all tariff-specific parameters also in the contract's `new` call. ## Creating the tariff The `InsuranceTarif` class provides a way to define an abstract insurance product. The most important parameters to be passed in the `InsuranceTarif$new()` call are: | | | |:-----|:----------| |**General settings for the Tariff** || | `name`, `tarif`, `desc` | IDs and human-readable descriptions of the insurance product. They are just used as labels and array keys, but do not influence the calculations.| | `type` | the most important parameter, defining the type of life insurance product (endowment, pure endowment, annuity, whole life insurance, dread-disease insurance, etc.)| |**Actuarial Bases for the Tariff** || | `mortalityTable` | a `MortalityTable` Object (package "MortalityTables"), providing the transition probabilities (typically death probabilities, but potentially also morbidity in dread-disease insurances)| | `i` | Guaranteed interest rate| | `costs`, `unitcosts` | passed a data structure for all cost parameters (see below)| | `premiumFrequencyLoading` | surcharge for premium payments more often than yearly (as a named list)| | `premiumRefund` | how much of the (gross) premium paid is returned upon death (often provided e.g. in deferred annuities or pure endowments with no fixed death benefit)| | `tax` |insurance tax |**Benefit calculation** || | `surrenderValueCalculation` | can be passed a hook function that calculates the surrender value given the current reserves at each time step| A typical call looks like the following for a pure endowment with gross premium refund upon death and a linearly decreasing surrender penalty: ```{r TarifDefinition, message = F} Tarif.PureEnd = InsuranceTarif$new( name = "Example Tariff - Pure Endowment", type = "pureendowment", tarif = "PE1-RP", desc = "A pure endowment with regular premiums (standard tariff)", mortalityTable = mort.AT.census.2011.unisex, i = 0.005, # Costs: 4% acquisition, where 2.5% are zillmered, 5\% of each premium as beta costs, # 1%o administration costs of the sum insured over the whole contract period costs = initializeCosts(alpha = 0.04, Zillmer = 0.025, beta = 0.05, gamma.contract = 0.001, gamma.paidUp = 0.001), unitcosts = 10, # Yearly premiums get no surcharge, monthly premiums add +4% premiumFrequencyLoading = list("1" = 0, "12" = 0.04), premiumRefund = 1, # Full gross premium refund upon death tax = 0.04, # 4% insurance tas surrenderValueCalculation = function(surrenderReserve, params, values) { n = params$ContractData$policyPeriod # Surrender Penalty is 10% at the beginning and decreases linearly to 0% surrenderReserve * (0.9 + 0.1 * (0:n)/n) } ) ``` Many parameters do not need to be given explicitly, but instead use sensible defaults (like the `premiumPeriod`, which by default equals the whole contract period, i.e. regular premium payments over the whole contract duration). To create a similar tariff with some changes, one can call the `createModification` method of the `InsuranceTarif` clas, which takes as arguments all insurance parameters that should be changed for the new tarif. To create a single-premium version of the pure endowment shown above, one can simply use a call like: ```{r TarifDefinitionSP} Tarif.PureEnd.SP = Tarif.PureEnd$createModification( name = "Example Tariff - Pure Endowment (SP)", tarif = "PE1-SP", desc = "A pure endowment with single premiums", premiumPeriod = 1 ) ``` ### Sample tariffs for the most common life insurance types For the examples in the remainder of this vignette, we can create some more example tariffs covering the most common types of life insurance. **General definitions for all tariffs** ```{r TarifDefinitions.All,message = F} library(MortalityTables) mortalityTables.load("Austria_Census") mortalityTables.load("Austria_Annuities_AVOe2005R") # Costs: 4% acquisition, where 2.5% are zillmered, 5\% of each premium as beta costs, # 1%o acquisition costs of the sum insured over the whole contract period example.Costs = initializeCosts( alpha = 0.04, Zillmer = 0.025, beta = 0.05, gamma.contract = 0.001, gamma.paidUp = 0.001 ) example.Surrender = function(surrenderReserve, params, values) { n = params$ContractData$policyPeriod # Surrender Penalty is 10% at the beginning and decreases linearly to 0% surrenderReserve * (0.9 + 0.1 * (0:n)/n) } ``` **Endowment** ```{r TarifDefinitions.All.End} Tarif.Endowment = InsuranceTarif$new( name = "Example Tariff - Endowment", type = "endowment", tarif = "EN1", desc = "An endowment with regular premiums", mortalityTable = mort.AT.census.2011.unisex, i = 0.005, costs = example.Costs, unitcosts = 10, tax = 0.04, # 4% insurance tax surrenderValueCalculation = example.Surrender ) ``` **Whole / Term Life Insurance** ```{r TarifDefinitions.All.Life} Tarif.Life = InsuranceTarif$new( name = "Example Tariff - Whole/Term Life", type = "wholelife", tarif = "Life1", desc = "A whole or term life insurance with regular premiums", mortalityTable = mort.AT.census.2011.unisex, i = 0.005, costs = example.Costs, unitcosts = 10, tax = 0.04, # 4% insurance tax surrenderValueCalculation = example.Surrender ) ``` **Immediate Annuity (single premium)** ```{r TarifDefinitions.All.ImmAnnuity} Tarif.ImmAnnuity = InsuranceTarif$new( name = "Example Tariff - Immediate Annuity", type = "annuity", tarif = "Ann1", desc = "An annuity with single-premium", premiumPeriod = 1, mortalityTable = AVOe2005R.unisex, i = 0.005, costs = example.Costs, tax = 0.04 # 4% insurance tax ) ``` **Deferred Annuity** ```{r TarifDefinitions.All.DefAnnuity} # Premium periods and deferral periods can also be given as a function of other # contract parameters (like the age at contract inception, etc.) Tarif.DefAnnuity = InsuranceTarif$new( name = "Example Tariff - Deferred Annuity", type = "annuity", tarif = "Life1", desc = "A deferred annuity (life-long payments start at age 65) with reg. premiums", policyPeriod = function(params, values) { 120 - params$ContractData$age}, deferralPeriod = function(params, values) { 65 - params$ContractData$age}, premiumPeriod = function(params, values) { 65 - params$ContractData$age}, mortalityTable = AVOe2005R.unisex, i = 0.005, costs = example.Costs, tax = 0.04, # 4% insurance tax surrenderValueCalculation = example.Surrender ) ``` **Dread-Disease Insurance** ```{r TarifDefinitions.All.DD} # An example dread-disease tariff, morbidity is assumed linearly increasing with age ddTable = mortalityTable.period(name = "Linear dread-disease table", ages = 0:100, deathProbs = 0:100/500) Tarif.DreadDisease = InsuranceTarif$new( name = "Example Tariff - Dread-Disease", type = "dread-disease", tarif = "DD1", desc = "A dread disease insurance with a lump-sum payment upon diagnosis", sumInsured = 50000, mortalityTable = mort.AT.census.2011.unisex, invalidityTable = ddTable, i = 0.005, costs = example.Costs, unitcosts = 10, tax = 0.04, # 4% insurance tax surrenderValueCalculation = example.Surrender ) ``` ## Creating a contract While the tariff describes the general product features, the contract object holds the data of a concrete contract. All insurance parameters (see section [All possible parameters and their default values]) can be given to override tarif defaults. However, the most important and often used parameters are: | | | |:-----|:---------| |**Information about insuree** | | `age` |the age of the insured person at contract start | `YOB` | the year of birth of the insured person (`age`, `YOB` and `contractClosing` are redundant, at most two need to be given). YOB is only relevant for cohort mortality tables. For period life tables (which are independent of the birth year of the person), this parameter is not needed. | | `sex` | relevant for sex-specific life tables (common in the past) | |**Contract details** | | `sumInsured` | the benefit when the insured event happens. Typically the lump sum for whole life insurances or endowments, or the (yearly) payment for annuities | | `policyPeriod` | the duration of the whole contract | | `premiumPeriod` | how long premiums are paid (1 for single-premiumcontracts, equal to `policyPeriod` (default) for regular premiums) | | `premiumFrequency` | how often premiums are paid within a year (e.g.1 for yearly premiums, 4 for quarterly, 12 for monthly) | | `contractClosing` | the starting date of the contract | | `deathBenefit` | gives the factor of death benefit relative to the sum insured / survival benefit of endowments (1 means equal death and survival probabilities), can also be a function with non-constant values over time | | `noMedicalExam`, `noMedicalExamRelative`, `sumRebate`, `extraRebate`, `premiumRebate` | various types of rebates or charges. They can either be defined in general functional form in the tariff to apply to all contracts, or given individually for each contract. For the details, when each of these rebates are applied, check the formula reference document.| For the pure endowments defined above, a typical contract would be created like this: ```{r Contract} contract.PureEnd = InsuranceContract$new( Tarif.PureEnd, age = 50, policyPeriod = 20, premiumFrequency = 12, sumInsured = 100000, contractClosing = as.Date("2020-07-01") ) ``` ```{r Contract.premiums,eval=F} contract.PureEnd$Values$premiums ``` ```{r Contract.premiumsOUT, echo = F} contract.PureEnd$Values$premiums %>% kable(digits=4) ``` ```{r Contract.premiumComposition,eval=F} contract.PureEnd$Values$premiumComposition ``` ```{r Contract.premiumCompositionOUT, echo = F} contract.PureEnd$Values$premiumComposition %>% as.data.frame() %>% rowid_to_column("t") %>% mutate(t = t-1) %>% select(t, charged, tax, loading.frequency, gross, gamma, beta, alpha, alpha.noZillmer, alpha.Zillmer, Zillmer, net, risk, savings) %>% pander ``` Due to the full premium refund in case of death, there is only very little biometric risk involved. If the premium refund is not included in the contract, then we have a negative biometric risk over the whole period (i.e. negative risk premium, because upon death the existing reserves is shared with the collective). The premium refund can be overridden directly in the contract call: ```{r ContractNoRefund} contract.PureEnd.NoRefund = InsuranceContract$new( Tarif.PureEnd, age = 50, policyPeriod = 20, premiumFrequency = 12, sumInsured = 100000, contractClosing = as.Date("2020-07-01"), premiumRefund = 0 ) ``` ```{r Contract.premiumsCode, eval = F} cbind(`With refund` = contract.PureEnd$Values$premiums, `Without refund` = contract.PureEnd.NoRefund$Values$premiums) ```{r Contract.premiumsOut, echo = F} cbind(`With refund` = contract.PureEnd$Values$premiums, `Without refund` = contract.PureEnd.NoRefund$Values$premiums) %>% pander ``` ```{r Contract.riskpremiumsCode, eval = F} cbind( `Gross premium with refund` = contract.PureEnd$Values$premiumComposition[,"gross"], `Gross premium w/o refund` = contract.PureEnd.NoRefund$Values$premiumComposition[,"gross"], `Risk premium with refund` = contract.PureEnd$Values$premiumComposition[,"risk"], `Risk premium w/o refund` = contract.PureEnd.NoRefund$Values$premiumComposition[,"risk"] ) ```{r Contract.riskpremiumsOut, echo = F} cbind( `Gross premium with refund` = contract.PureEnd$Values$premiumComposition[,"gross"], `Gross premium w/o refund` = contract.PureEnd.NoRefund$Values$premiumComposition[,"gross"], `Risk premium with refund` = contract.PureEnd$Values$premiumComposition[,"risk"], `Risk premium w/o refund` = contract.PureEnd.NoRefund$Values$premiumComposition[,"risk"] ) %>% as_tibble() %>% rowid_to_column("t") %>% mutate(t = t-1) %>% pander ``` To create a single-premium contract, one can either use the single-premium tarif defined above, or simply pass `premiumPeriod=1` to the call: ```{r Contract.SP} contract.PureEnd.SP1 = InsuranceContract$new( Tarif.PureEnd, age = 40, policyPeriod = 45, premiumPeriod = 1, sumInsured = 100000, contractClosing = as.Date("2020-07-01") ) contract.PureEnd.SP2 = InsuranceContract$new( Tarif.PureEnd.SP, age = 40, policyPeriod = 45, # premiumPeriod already set by tariff! sumInsured = 100000, contractClosing = as.Date("2020-07-01") ) all_equal(contract.PureEnd.SP1$Values$reserves, contract.PureEnd.SP2$Values$reserves) ``` ## Determining Sum Insured from the premium By default, the insurance contract is created for a given sum insured and the premiums are calculated accordingly. Sometimes, the reverse is needed: The premium (either actuarial gross premium, written premium before or after taxes) is given and the corresponding sum insured should be determined automatically. The `InsuranceContract` constructor / parameter set has an additional field `premium` to indicate the desired premium (written premium after tax by default) from which the sum insured is then calculated: ```{r PrescribePremium} # Premium calculated from sumInsured contract.End = InsuranceContract$new( Tarif.Endowment, age = 35, policyPeriod = 10, contractClosing = as.Date("2020-08-18"), sumInsured = 10000); # sumInsured derived from written premium contract.End.premium = InsuranceContract$new( Tarif.Endowment, age = 35, policyPeriod = 10, contractClosing = as.Date("2020-08-18"), premium = 1139.06); contract.End.premiumBeforeTax = InsuranceContract$new( Tarif.Endowment, age = 35, policyPeriod = 10, contractClosing = as.Date("2020-08-18"), premium = c(written_beforetax = 1095.25)); contract.End.premiumGross = InsuranceContract$new( Tarif.Endowment, age = 35, policyPeriod = 10, contractClosing = as.Date("2020-08-18"), premium = c(gross = 1085.25)); ``` ```{r PrescribePremiumOUTPUT,echo = FALSE} bind_rows( c(Contract = "contract.End", contract.End$Values$premiums[c("net", "Zillmer", "gross", "written_beforetax", "written")], sumInsured = contract.End$Parameters$ContractData$sumInsured), c(Contract = "contract.End.premium", contract.End.premium$Values$premiums[c("net", "Zillmer", "gross", "written_beforetax", "written")], sumInsured = contract.End.premium$Parameters$ContractData$sumInsured), c(Contract = "contract.End.premiumBeforeTax", contract.End.premiumBeforeTax$Values$premiums[c("net", "Zillmer", "gross", "written_beforetax", "written")], sumInsured = contract.End.premiumBeforeTax$Parameters$ContractData$sumInsured), c(Contract = "contract.End.premiumGross", contract.End.premiumGross$Values$premiums[c("net", "Zillmer", "gross", "written_beforetax", "written")], sumInsured = contract.End.premiumGross$Parameters$ContractData$sumInsured) ) ``` The final written premium can be directly passed as the `premium` argument. Other types of premium must be passed as a named number (i.e. a one-element vector with name "gross", "written_beforetax" or "written"). If a premium is prescribed, a `sumInsured` must not be given. If both are given, the sumInsured is used and the prescribed premium is silently ignored. The are cases, when the sumInsured cannot be derived from a prescribed premium. One relevant case is when a premium rebate depends on the sum insured, since in this case we need the sumInsured to calculate the rebate and thus the premium. All rebates, add-ons and other parameters will temporarily use a sumInsured=1 during the calculation of the actual sumInsured! ## Providing additional capital at contract inception Often, when a contract is changed significantly, this is modelled as a totally new contract, with the existing reserve provided as additional one-time payment for the follow-up contract. This is different from a single-premium contract, because the new contract can have regular premiums, just the existing reserve is used as the initial reserve of the new contract. The package provides the argument `initialCapital` to provide initial capital that is also included in the calculation of the premium of the contract. ```{r InitialCapital} # Contract with initial capital of 5.000 EUR contract.Endow.initialCapital = InsuranceContract$new( tarif = Tarif.Endowment, sumInsured = 10000, initialCapital = 5000, age = 40, policyPeriod = 10, contractClosing = as.Date("2020-09-01") ) # For comparison: Contract without initial capital of 5.000 EUR contract.Endow = InsuranceContract$new( tarif = Tarif.Endowment, sumInsured = 10000, age = 40, policyPeriod = 10, contractClosing = as.Date("2020-09-01") ) ``` Comparing the reserves, one can clearly see the initial capital used as initial reserve: ```{r InitialCapitalOUTPUT} data.frame( `Premium with initialCapital`= contract.Endow.initialCapital$Values$premiumComposition[,"charged"], `Premium without initialCapital`= contract.Endow$Values$premiumComposition[,"charged"], `Res.with initialCapital`= contract.Endow.initialCapital$Values$reserves[,"contractual"], `Res.without initialCapital`= contract.Endow$Values$reserves[,"contractual"] ) ``` ## Premium Waivers After a while, many customers do not want to pay premiums for the contract any more and convert the contract to a paid-up contract. The unit benefit cash flows from that moment on stay the same, but the sum insured is adjusted, so that the existing reserve is able to cover all future benefits and costs. Furthermore, paid-up contracts typically have differen costs / loadings. Additionally, the surrender penalty is usually applied to the reserve before the conversion. Waiving premiums and recalculating the sum insured is very easy, one just calls the method `InsuranceContract$premiumWaiver(t = ..)` on the existing contract. ```{r Contract.PureEndPrf, results="hide"} contract.PureEnd.NoRefund.Prf = contract.PureEnd.NoRefund$clone()$premiumWaiver(t = 7) contract.PureEnd.NoRefund.Prf$Values$reserves ``` ```{r Contract.PureEndPrfOUT, echo=F} contract.PureEnd.NoRefund.Prf$Values$reserves %>% pander ``` Notice that the contract changes are made directly to the contract ("reference semantics"). This is different from the typical behavior of R, where any change to e.g. a data.frame leaves the original data.frame intact and instead returns a modified copy. # Calculation Approach ## Valuation The calculation of all contract values is controlled by the function `InsuranceContract$calculateContract()` (using methods of the `InsuranceTarif` object) and follows the following logic: 1. First the **contingent (unit) cash flows** and the **transition probbilities** are determined. 2. The **actuarial equivalence principle** states that at time of inception, the (net and gross) premium must be determined in a way that the present value of the future benefits and costs minus the present value of the future premiums must be equal, i.e. in expectation the future premiums ove the whole lifetime of the contract will exactly cover the benefits and costs. Similarly, at all later time steps, the difference between these two present values needs to be reserved (i.e. has already been paid by the customer by previous premiums). 2. This allows the premiums to be calculated by first calculating the **present values** for all of the **benefit and costs cash flow** vectors. 3. The formulas to calculate the gross, Zillmer and net **premiums** involve simple linear combinations of these present values, so the **coefficients of these formulas** are determined next. 4. With the coefficients of the premium formulas calculated, all **premiums can be calculated** (first the gross premium, because due to potential gross premium refunds in case of death, the formula for the net premium requires the gross premium, which the formula for the gross premium involves no other type of premuim). 5. With premiums determined, all unit cash flows and unit present values can now be expressed in monetary terms / as **absolute cash flows** (i.e. the actual Euro-amount that flows rather than a percentage). 6. As described above, the difference between the present values of premiums and present values of benefits and costs is defined as the required amount of reserves, so the **reserves (net, gross, administration cost, balance sheet)** and all values derived from them (i.e. surrender value, sum insured in case of premium waiver, etc.) are calculated. 7. The **decomposition of the premium** into parts dedicated to specific purposes (tax, rebates, net premium, gross premium, Zillmer premium, cost components, risk premium, savings premium, etc.) can be done once the reserves are ready (since e.g. the savings premium is defined as the difference of discounted reserves at times $t$ and $t+1$). 8. If the contract has **(discretionary or obligatory) profit sharing**B mechanisms included, the corresponding [ProfitParticipation] object can calculate that profit sharing amounts, once all guaranteed values are calculated. This can also be triggered manually (with custom profit sharing rates) by calling the methods `InsuranceContract$profitScenario()` or `InsuranceContract$addProfitScenario()`. ## Cash Flows An insurance contract is basically defined by the (unit) cash flows it produces: \itemize{ \item **Premium payments** (in advance or in arrears) at each timestep \item **Survival payments** at each timestep \item **Guaranteed payments** at each timestep \item **Death benefits** at each timestep \item **Disease benefits** at each timestep } Together with the transition probabilities (mortalityTable parameter) the present values can be calculated, from which the premiums follow and finally the reserves and a potential profit sharing. For example, a _**term life insurance with regular premiums**_ would have the following cash flows: * premium cash flows: 1, 1, 1, 1, 1, ... * survival cash flows: 0, 0, 0, 0, 0, ... * guaranteed cash flows: 0, 0, 0, 0, 0, ... * death benefit cash flows: 1, 1, 1, 1, 1, ... A _**single-premium term life insurance**_ would look similar, except for the premiums: * premium cash flows: 1, 0, 0, 0, 0, ... A _**pure endowment**_ has no death benefits, but a survival benefit of 1 at the maturity of the contract: * premium cash flows: 1, 1, 1, 1, 1, ... * survival cash flows: 0, 0, ..., 0, 1 * guaranteed cash flows: 0, 0, 0, 0, 0, ... * death benefit cash flows: 0, 0, 0, 0, 0, ... An _**endowment**_ has also death benefits during the contract duration: * premium cash flows: 1, 1, 1, 1, 1, ... * survival cash flows: 0, 0, ..., 0, 1 * guaranteed cash flows: 0, 0, 0, 0, 0, ... * death benefit cash flows: 1, 1, 1, 1, 1, ... A _**(deferred) annuity**B_ has premium cash flows only during the deferral peroid and only survival cash flows during the annuity payment phase. Often, in case of death during the deferral period, all premiums paid are refunded as a death benefit.: * premium cash flows: 1, 1, ..., 1, 0, 0, 0, ... * survival cash flows: 0, 0, ..., 0, 1, 1, 1,... * guaranteed cash flows: 0, 0, 0, 0, 0, ... * death benefit cash flows: 1, 2, 3, 4, 5, ..., 0, 0, ... A _**terme-fix insurance**_ has a guaranteed payment at maturity, even if the insured has already died. The premiums, however, are only paid until death (which is not reflected in the contingent cash flows, but rather in the transition probabilities): * premium cash flows: 1, 1, 1, 1, ..., 1 * survival cash flows: 0, 0, 0, 0, ..., 0 * guaranteed cash flows: 0, 0, 0, ..., 0, 1 * death benefit cash flows: 0, 0, 0, 0, ..., 0 # Cost structure Costs of an insurance contracts can have various forms and bases. The `InsuranceContract` class provides all common types of costs: * Type of cost: Acquisition (Alpha-costs), Zillmer, Beta-costs, Administration (Gamma-costs during / after premiums and for paid-up contracts) * Basis for costs: SumInsured, Premium Sum, Gross Premium, Net Premium, Reserve, constant costs * Duration of costs: Once (up-front), during premium payments, after premium payments, during whole contract The cost structure generated by `initializeCosts()` is a three-dimensional array with the above-mentioned coordinates: ```{r costStructureDimensions} initializeCosts() %>% dimnames ``` The most common types of cost can be directly given in the call to `initializeCosts()`, but for some uncommon combinations, one can directly fill any of the fields in the three-dimensional array manually. ```{r costExample, eval=F} initializeCosts(alpha = 0.04, Zillmer = 0.025, beta = 0.05, gamma.contract = 0.001) # the above is the short form of: costs.Bsp = initializeCosts() costs.Bsp[["alpha", "SumPremiums", "once"]] = 0.04 costs.Bsp[["Zillmer", "SumPremiums", "once"]] = 0.025 # German Zillmer maximum costs.Bsp[["beta", "GrossPremium", "PremiumPeriod"]] = 0.05 costs.Bsp[["gamma", "SumInsured", "PolicyPeriod"]] = 0.001 ``` These costs parameters are immediately converted to the corresponding cash flows when the contract is created. In the above example of a pure endowment (with premium waiver at time 7), the absolute cost cash flows are: ```{r costCashFlowsCode, eval=F} contract.PureEnd.NoRefund$Values$absCashFlows ``` ```{r costCashFlows, echo=F} contract.PureEnd.NoRefund$Values$absCashFlows[1:11,] %>% select(alpha, Zillmer, beta, gamma, gamma_nopremiums, unitcosts) %>% pander() ``` In addition to these standardized costs, which are included in the expense-loaded premium (sometimes also called the "(actuarial) gross premium"), there are certain loadings and rebates that are applied after the expense-loaded premium $BP_x$: $$P_x = \left\{ (BP_x + oUZu - SuRa) \cdot VS \cdot (1-VwGew) + unitC\right\} \cdot \left(1-PrRa-VwGew_{StkK}-PartnerRa\right)\cdot \left(1+uz(k)\right) \cdot (1+tax)$$ with the following surcharges and rebates: * $oUZu$ ... Surcharge for contracts without medical exam (parameter `noMedicalExam`) * $SuRa$ ... Sum rebate (depending on the sum insured) (parameter `sumRebate`) * $VwGew$ ... Advance profit participation (as premium rebate) (parameter `advanceProfitParticipation`) * $unitC$ ... Unit costs per year (only during premium payments) (parameter `unitcosts`) * $PrRa=PrRa(BP)$ ... Premium rebate (depending on premium amount) (parameter `premiumRebate`) * $VwGew_{StkK}$ ... Advance profit participation (as premium rebate, on premium after unit costs) (parameter `advanceProfitParticipationInclUnitCost`) * $PartnerRa$ ... Partner rebate on the premium (for multiple similar contracts) (parameter `partnerRebate`) * $uz(k)$ ... Frequency charge for monthly/quarterly/half-yearly premium payments instead of yearly premiums (parameter `premiumFrequencyLoading`) * $tax$ ... Insurance tax (0% in most countries, 4% or 11% in Austria) (parameter `tax`) ## Frequency charges Typically, an insurance premium is calculated with yearly premiums paid in advance. If premiums are paid more often per year (Parameter `premiumFrequency` or `benefitFrequency` set to a value larger than 1, typically 2 for half-yearly, 4 for quarterly, or 12 for monthly payments), part of the interest during the year is lost, as well as the premiums for the remainder of the year if the insured event happens. So usually there is some kind of extra charge included: * A frequency charge is added on the gross premium => Parameter `premiumFrequencyLoading` and `benefitFrequencyLoading` are set to a list with keys "1", "2", "4" and "12" and values that correspond to the extra charge (as a factor on the gross premium, i.e. a percentage). * The premium or benefit payments are calculated (approximately) as payments $k$-times per year using yearly death / incidence probabilities. This calculation is typically using some approximations in $k$ => Parameter `premiumFrequencyOrder` and `benefitFrequencyOrder` set to a value larger than 0. * Contracts with $k>1$ have higher cost loadings on the gross premium. => Parameter `costs` is implemented a function with signature $function(params, values)$, which accesses `params$ContractData$premiumFrequency` to return different expense rates for different premium payment frequencies. * If the guaranteed interest rate is 0\%, the exact moment of payment during a year is irrelevant for the guaranteed values. However, if a yield larger than 0\% is achieved, it is assigned to the contract via profit participation. So it makes sense adjust profit participation rates depending on the premium frequency rather than modifying the premium. The higher the profit participation, the higher the effect of the $k$-th yearly premium payments and the higher the modification of the rates can be. => Parameter `getInterestProfitRate` of the profit scheme can be implemented as a function that modifies the rate depending on the premium or benefit frequency (similar to the cost adjustment mentioned in the previous item). ```{r FrequencyCharges} Tarif.Life.FrequencyLoading = Tarif.Life$createModification( name = "Term life (frequency loading)", premiumFrequencyLoading = list("1" = 0.0, "2" = 0.01, "4" = 0.015, "12" = 0.02) ) Tarif.Life.FrequencyApprox1 = Tarif.Life$createModification( name = "Term life (k-th yearly, approx. 1.Ord.)", premiumFrequencyOrder = 1 ) Tarif.Life.FrequencyApprox2 = Tarif.Life$createModification( name = "Term life (k-th yearly, approx. 2.Ord.)", premiumFrequencyOrder = 2 ) Tarif.Life.FrequencyApprox3 = Tarif.Life$createModification( name = "Term life (k-th yearly, exact)", premiumFrequencyOrder = Inf ) Tarif.Life.FrequencyExpense = Tarif.Life$createModification( name = "Term life (modified gamma costs)", costs = function(params, values) { switch (toString(params$ContractData$premiumFrequency), "12" = initializeCosts(alpha = 0.04, Zillmer = 0.025, beta = 0.05, gamma.contract = 0.00127, gamma.paidUp = 0.001), "4" = initializeCosts(alpha = 0.04, Zillmer = 0.025, beta = 0.05, gamma.contract = 0.00119, gamma.paidUp = 0.001), "2" = initializeCosts(alpha = 0.04, Zillmer = 0.025, beta = 0.05, gamma.contract = 0.0011, gamma.paidUp = 0.001), initializeCosts(alpha = 0.04, Zillmer = 0.025, beta = 0.05, gamma.contract = 0.001, gamma.paidUp = 0.001) ) } ) ``` Of course, the loadings and costs mentioned above are not necessarily mathematically derived to offset the interest effect of k-th yearly payments. Rather, they are often simply decided by the management. Thus, the three approaches implemented here can have quite different results: ```{r FrequencyCharges.Grid} contractGridPremium( axes = list(tarif = c(Tarif.Life.FrequencyLoading, Tarif.Life.FrequencyApprox1, Tarif.Life.FrequencyApprox2, Tarif.Life.FrequencyApprox3, Tarif.Life.FrequencyExpense), premiumFrequency = c(1, 2, 4, 12)), age = 40, policyDuration = 20, sumInsured = 100000, contractClosing = as.Date("2020-09-01") ) %>% kableTable ``` ## Security loadings Although most mortality tables and expense loadings already have a certain amount of security margins included, often a tariff (mostly protection products) adds an additional security loading directly to the net present value of the benedits. This can be easily implemented using the parameter `security`: ```{r Protection.Security} contractGridPremium( axes = list(age = seq(30, 60, 10), security = 10*(0:5)/100), tarif = Tarif.Life, policyDuration = 20, sumInsured = 100000, contractClosing = as.Date("2020-09-01") ) %>% kableTable(digits = 2) ``` # Creating premium and contract grids When developing a new product or comparing different products, it is often required to create tables of premiums or reserves for a product/tarif where just some of the parameters (like age, sum insured or maturity) change. For this purpose, this package provides two functions to create two- or higher-dimensional grids of contracts with each dimension representing one of the parameters varying. * `contractGrid()` creates a (two- or higher-dimensional) grid of `InsuranceContract` object * `contractGridPremium()` creates a grid of the premiums The grid is defined by the `axes` argument to the `contractGrid()` call. This is a named list giving all parameters that should vary inside the grid. Any of the parameters of the `InsuranceContract$new()` constructor can be used in the axes. For example, one can compare multiple tariffs or multiple varying pararameters. Let us look at the pure endowment above, which we implemented as a single-premium variant and a variant with regular premiums, both of which have a potential (partial or full) premium refund in case of death. How do the premiums of these contracts compare and how do the premiums depend on the premium refund proportion? ```{r Grid.Endowment.compare, results = "hide"} grd = contractGridPremium( axes = list(tarif = c(Tarif.PureEnd, Tarif.Endowment, Tarif.PureEnd.SP), premiumRefund = c(0, 0.5, 1)), age = 50, policyPeriod = 20, sumInsured = 10000, contractClosing = as.Date("2020-09-01") ) grd ``` ```{r Grid.Endowment.compareOUT, echo = F} grd %>% kableTable ``` The default implementation of `contractGridPremium` returns the written premium, but one can also choose other types of premiums to display, or even other contract values (like reserves). If one needs to investigate multiple values, it is better to first create a grid of insurance contract objects and store it, so that the call to `contractGridPremium` does not have to re-calculate the same contracts over and over again, extract just one premium and discard the whole contract. ```{r Grid.Endowment.compareOther} grd = contractGrid( axes = list(tarif = c(Tarif.PureEnd, Tarif.Endowment, Tarif.PureEnd.SP), premiumRefund = c(0, 0.5, 1)), age = 50, policyPeriod = 20, sumInsured = 10000, contractClosing = as.Date("2020-09-01") ) ``` ```{r Grid.Endowment.compareOtherG1, eval = F} # Compare net premiums without loadings: contractGridPremium(grd, premium = "net") ``` ```{r Grid.Endowment.compareOtherG1Out, echo = F} contractGridPremium(grd, premium = "net") %>% kableTable ``` ```{r Grid.Endowment.compareOtherG2, eval = F} # Compare premium sums over the whole contract period (all contracts have the same sumInsured) contractGridPremium(grd, .fun = function(c) {with(c$Values, unitPremiumSum * premiums["written"]) }) ``` ```{r Grid.Endowment.compareOtherG2Out, echo = F} # Compare premium sums over the whole contract period (all contracts have the same sumInsured) contractGridPremium(grd, .fun = function(c) {with(c$Values, unitPremiumSum * premiums["written"]) }) %>% kableTable(digits = 2) ``` ```{r Grid.Endowment.compareOtherG3, eval = F} # Compare risk premiums at time t=10 (the 11th row of the premium decomposition) contractGridPremium(grd, .fun = function(c) {c$Values$premiumComposition[11, "risk"]}) ``` ```{r Grid.Endowment.compareOtherG3Out, echo = F} # Compare risk premiums at time t=10 (the 11th row of the premium decomposition) contractGridPremium(grd, .fun = function(c) {c$Values$premiumComposition[11, "risk"]}) %>% kableTable(digits = 2) ``` ```{r Grid.Endowment.compareOtherG4, eval = F} # Compare present value of all benefits and refunds (without costs) at time t=0 contractGridPremium(grd, .fun = function(c) {c$Values$absPresentValues[1, "benefitsAndRefund"]}) ``` ```{r Grid.Endowment.compareOtherG4Out, echo = F} # Compare present value of all benefits and refunds (without costs) at time t=0 contractGridPremium(grd, .fun = function(c) {c$Values$absPresentValues[1, "benefitsAndRefund"]}) %>% kableTable(digits = 2) ``` Other useful examples of grid comparisons include e.g. the effect of the interest rate and the mortality table on the premiums: ```{r Grid.Protection, results ="hide"} grd = contractGridPremium( axes = list(mortalityTable = mort.AT.census["m", -(1:10)], i = c(0, 0.005, 0.01), age = c(30, 45, 60), policyPeriod = c(10, 20)), tarif = Tarif.Life, contractClosing = as.Date("2020-09-01"), sumInsured = 10000 ) grd ``` ```{r Grid.ProtectionOUT, echo=F, results="asis"} for (a in dimnames(grd)[[3]]) { for (d in dimnames(grd)[[4]]) { cat("\n", "* ", names(dimnames(grd))[[3]], "=", a, ", ", names(dimnames(grd))[[4]], "=", d, "\n\n", sep = "") # cat(grd[,,d ] %>% as.data.frame() %>% rownames_to_column("age \\| policyPeriod") %>% pander(digits = 7, round = 2, style = "rmarkdown")) cat(grd[,, a, d] %>% kableTable(digits = 2), "\n") } } ``` # Exporting contract data to Excel The LifeInsureR package also provides a function to export a given contract to a spreadsheet in Excel format: * `exportInsuranceContract.xlsx(contract, filename)` This function takes the contract and exports all data.frames in the contract's `contract$Values` data list as separate tabs to a spreadsheet file. It also adds some nice formatting and additional markup. For contracts with multiple contract blocks (e.g. dynamic increases / sum increases or riders), the main contract and all its child blocks are exported as well. The tab containing the premiums and the premium calculation coefficients even include the formulas, so one can in theory play around with the parameters to see how the premium changes. If the contract has profit participation features and some profit scenarios have been added, they are exported as well. Notice, however, that the Excel export is in general a static file containing the current state of the contract and all its values (cash flows, present values, premiums, reserves, profit participation, etc.) as well as the most important parameters and an overview of the history of the contract. ```{r ExcelExport,eval=F} contract.exportExample = contract.PureEnd.NoRefund$clone()$ addDynamics(t = 3, SumInsuredDelta = 10000)$ addDynamics(t = 5, SumInsuredDelta = 15000)$ addDynamics(t = 10, SumInsuredDelta = 15000)$ addDynamics(t = 14, SumInsuredDelta = 10000) exportInsuranceContract.xlsx(contract.exportExample, filename = "Example_PureEndowment_Dynamics.xlsx") ``` # Creating examples for the Austrian Financial Market Authority When introducing a new tariff, an Austrian insurance company has to submit a detailled mathematical description (so-called "Versicherungsmathematische Grundlagen") of the tariff to the Financial Market Authority (FMA), which have to follow the [regulation on actuarial bases for life insurance -- LV-VMGV](https://www.ris.bka.gv.at/GeltendeFassung.wxe?Abfrage=Bundesnormen&Gesetzesnummer=20009298). The sections and contents of this document are strictly defined in the regulation, and in addition to the formulas of the tariff, an example contract has to be calculated with the following key parameters: * age 35, duration 30 years (15 years for protection tariffs) * sum insured 100 000 Euro / monthly annuity 1000 Euro for annuities * yearly premiums during the whole contract period (defereal period of 30 years for annuities) * intermediate values (reserves, etc.) have to be calculated at $t=10$. Given a tariff and a derived contract (with the above key parameters), this package provides two functions to calculate and print / export the required values either to the console / screen or to a file: * `showVmGlgExamples(contract, prf = 10, t = 10, t_prf = 12, file = "", ...)` * `exportInsuranceContractExample(contract, prf = 10, outdir = ".", basename=NULL, extraname = NULL, ...)` The function `showVmGlgExamples` calculates all required values for the actuarial bases document in the required order (including values after a premium waiver at the given time). If a filename is given, the output is exported to that text file, otherwise it is simply printed out in the R console: ```{r VmGlgExample} VMGL.contract = InsuranceContract$new( Tarif.PureEnd, age = 35, policyPeriod = 30, premiumFrequency = 1, sumInsured = 100000, contractClosing = as.Date("2020-07-01") ) showVmGlgExamples(VMGL.contract) ``` The `exportInsuranceContractExample` provides no new functionality, but combines the Excel export feature and the regulatory example together. For a given contract it will create three files: * Excel export of the contract * Excel export of the contract with a premium waiver applied at the given time * The regulatory example (with premium waiver at the same time as in the Excel export) in a text file One can give a base name and an extra name to distinguish different calculations in the file names. # Contracts combining multiple contract layers / slices In real life, an insurance contract is not merely signed initially and then left untouched until it expires or is surrendered. Rather, some contracts already have an automatic increase of the sumInsured or the premium (depending usually on some kind of observed consumer price index) included in the contract. Other contracts have additional biometric riders like an additional death or disability cover. Other contracts are extended after their expiration, using the existing reserve as a one-time payment (`initialCapital`) for the follow-up contract. In all these cases, the original contract can be calculated as an `InsuranceContract`, but the additional dynamic increases, additional riders or extensions are mathematically calculated as separate contracts (potentiall using different tariffs / types of insurance), although most parameters are shared from the original main contract. In addition to modelling one particular tariff, the LifeInsuranceContract class can also act as a wrapper to bundle multiple related contracts / contract slices together. The class provides several methods for this: * `$addDynamics(t, NewSumInsured, SumInsuredDelta, id, ...)`: Include (at time `t`) a dynamic increase of premium or sumInsured, but with the same basic parameters (age, tariff, maturity, interest rate, etc.) as the main contract. The increase can be given either as the new total sumInsured or as the increase in the sumInsured caused by that one increase. Other parameters given as `...` are passed on to the `InsuranceContract$new` constructor of the layer for the dynamic increase. This also means that one can potentially override all parameters for the increase, including the tariff or the interest rate. * `$addExtension(t = NULL, policyPeriod, ...)` After the original contracts maturity, append a follow-up contract (by default paid-up, i.e. no new premiums are paid) that uses the existing reserve as initial capital. By default, no further premiums are paid and the sumInsured is calculated from the existing reserve and the tariff of the extension. One can, however, also provide either a sumInsured or a premium of the contract extension. In that case, the premium or the sumInsured will be calculated, using the existing reserves as initialCapital. * `$addBlock(id = NULL, block = NULL, t, ...)` Generic function to add a child block to the contract. If a block (object of type `LifeInsuranceContract` is passed, it is inserted and flagged as starting at time `t`. If no block is passed, a new insurance contract is created using the arguments passed as `...`, combined with the parameters of the main contract. If `t>0`, the child block starts later than the original contract. It is also possible that the child block extends beyond the maturity of the original contract (e.g. contract extensions are implemented this way). In these case, the main contract will have several child blocks (also LifeInsuranceContract objects), and the values of the main contract object will be the aggregated values of all its children, rather than the results of a calculation from an underlying tariff. ## Dynamic increases To increase the sum insured or premium by a given value () ```{r contractLayers} # Contract with initial capital of 5.000 EUR ctr.dynInc = InsuranceContract$new( tarif = Tarif.Endowment, sumInsured = 10000, age = 40, policyPeriod = 10, contractClosing = as.Date("2020-09-01") )$ addDynamics(t = 1, SumInsuredDelta = 1000)$ addDynamics(t = 5, NewSumInsured = 15000)$ addDynamics(t = 8, SumInsuredDelta = 4000) ctr.dynInc$Values$basicData ``` As seen in this table, the sum insured increases and the premium with it. The `PremiumPayment` column is no longer a 0/1-column indicating whether a premium is paid or not, but rather is the number of blocks/layers where a premium is paid. The individual blocks can be accessed with the `contract$blocks` list: ```{r contractLayers.blocks} for (b in ctr.dynInc$blocks) { cat(paste0("Block: ", b$Parameters$ContractData$id, ", starts at t=", b$Parameters$ContractData$blockStart, ", policyPeriod=", b$Parameters$ContractData$policyPeriod, "\n")) } ``` Each block is formally handled like a separate contract, each starting at its own time `t=0`. The over-all contract then takes care to correctly shift the child blocks to the time relative to the parent block, before aggregating the data: ```{r contractLayers.blocks.data} ctr.dynInc$blocks$Hauptvertrag$Values$basicData ctr.dynInc$blocks$dyn1$Values$basicData ctr.dynInc$blocks$dyn2$Values$basicData ctr.dynInc$blocks$dyn3$Values$basicData ``` ## General biometric riders Instead of adding a dynamic increase, which typically uses the same tariff as the main contract, it is also possible to bundle e.g. a protection rider to a saving product. The savings product and the protection rider are calculated individually as child blocks, and the overall values of the contract are obtained by aggregating the values from the two children (savings and protection part). Of course, in this scenario, the combined sumInsured of the overall contract is not meaningful, but the sumInsured of the individual blocks is. ```{r addBlock.rider} ctr.main = InsuranceContract$new( tarif = Tarif.Endowment, sumInsured = 10000, age = 40, policyPeriod = 10, contractClosing = as.Date("2020-09-01") ) ctr.Rider = InsuranceContract$new( tarif = Tarif.L71U, sumInsured = 100000, age = 40, policyPeriod = 10, contractClosing = as.Date("2020-09-01") ) ctr.main$addBlock(block = ctr.Rider) ctr.withRider = InsuranceContract$new( tarif = Tarif.Endowment, sumInsured = 10000, age = 40, policyPeriod = 10, contractClosing = as.Date("2020-09-01") )$ addBlock(tarif = Tarif.L71U, sumInsured = 100000, age = 40, policyPeriod = 10, contractClosing = as.Date("2020-09-01")) ``` ## Extending a contract beyond its maturity When a contract expires, many companies offer premium-free contract extensions, where the existing reserve is used as initial reserve for a follow-up contract (possibly with new terms and parameters like interest rate or mortalities). Instead of modifying the original contract and re-calculating it, it is easier to model the extension as a new block with the existing reserve given as \code{initialCapital}. The extension will be calculated like a standalone-contract and the overall contract will aggregate the values from the original contract and the extension. As the extension is a separate contract object, one can pass all contract parameters to the \code{$addExtension} method. The original premiumPeriod of the main contract is used, so by default the extension will be a premium-free extension, where the sumInsured is calculated from the existing reserve and the benefits and costs of the extensions' tariff. To create a premium-free extension explicitly, one can pass \code{premiumPeriod=0} (which is the default anyway). To create an extension with regular (or single) premium payments, one can pass either a \code{sumInsured} or a \code{premium} to provide the sum insured and the premium and calculate the other from the given value ```{r contractExtension} # original contract, expiring after 20 years ContractA = InsuranceContract$new( tarif = Tarif.Endowment, age = 40, policyPeriod = 20, sumInsured = 10000, contractClosing = as.Date("2000-07-01") ) # premium-free extension ContractB = ContractA$clone()$ addExtension(id = "Verlaengerung1", contractPeriod = 5, premiumPeriod = 0) # sumInsured calculated from existing reserve: ContractB$blocks$Verlaengerung1$Parameters$ContractData$sumInsured ContractB$Values$basicData # extension with given sumInsured resulting in 0 (gross) premiums ContractC = ContractA$clone()$ addExtension(id = "Verlaengerung1", contractPeriod = 5, sumInsured = 10723.07973354) ContractC$blocks$Verlaengerung1$Values$premiums[["gross"]] ContractC$Values$basicData # extension with increased sumInsured: real premiums are charged, reserves start from the existing reserve: ContractD = ContractA$clone()$ addExtension(id = "Verlaengerung1", contractPeriod = 5, sumInsured = 20000) ContractD$Values$basicData # extension with regular premiums, which are given: sumInsured is calculated from it, reserves start from the existing reserve: ContractD = ContractA$clone()$ addExtension(id = "Verlaengerung1", contractPeriod = 5, premium = 597.8771) ContractD$Values$basicData ``` # Handling contracts with increases While many insurance contracts have a fixed sum insured and constant premium, many contracts include some kind of adjustment to account for inflation. There are various ways to achieve such an adjustment: * The initial contract already includes a planned increase in the benefits by a pre-determined factor $(1+s)$ each year, premiums are constant over the whole duration. * The initial contract has fixed sum insured, but the premiums increase by a factor $(1+s)$ each year due to salary increases. * "Dynamic increases": The initial contract has fixed sum insured with fixed regular premiums. However, every year (or triggered based on an inflation or consumer price index) the sum insured is increased by a certain amount (either fixed or by the same percentage as the index) and the premiums are increased accordingly. Internally, this is represented by a second, shorter contract covering only the increase in sumInsured, from which the additional premium can be calculated according to the tariff. The LifeInsuranceContract package provides functionality for each of these increases. All three increases can in theory be combined in the same contract, although in practice this usually does not happen and at most one kind of increase is included in a contract ## Fixed yearly premium increases With this kind of increases, the initial contract valuation (i.e. the determination of the premium at contract inception) already takes into account that the premium will not stay constant over the whole period, but increases by a constant factor each year. The sum insured is calculated by the equivalence principle so that the expected present value of all future benefits and costs equals the expected present value of all future (i.e. increasing) premium payments. This type of yearly premium increase by a fixed factor can be implemented by the parameter: * `premiumIncrease` ... The factor, by which the premium increases yearly. Default is 1.0 (no increase in premium). In the following example, we create a 10-year endowment contract with constant premiums over the whole period and another one with idential parameters except that the premium increases by 4\% each year: ```{r PremiumIncrease.Endowment, results = "hide"} # For comparison: Contract with constant premiums contract.Endow.Constant = InsuranceContract$new( tarif = Tarif.Endowment, sumInsured = 10000, age = 50, policyPeriod = 10, contractClosing = as.Date("2020-09-01") ) # Contract with 4% yearly premium increase and same sum insured contract.Endow.PremInc = InsuranceContract$new( tarif = Tarif.Endowment, sumInsured = 10000, premiumIncrease = 1.04, age = 50, policyPeriod = 10, contractClosing = as.Date("2020-09-01") ) premium.comparison = data.frame( `Sum Insured` = contract.Endow.Constant$Values$basicData[,"SumInsured"], `Constant Premium` = contract.Endow.Constant$Values$basicData[,"Premiums"], `4% Yearly Increase` = contract.Endow.PremInc$Values$basicData[,"Premiums"], check.names = F ) ``` ```{r PremiumIncrease.EndowmentOut, results = "asis"} premium.comparison %>% pander ``` ## Fixed yearly benefit increases with constant premium With this kind of increases, the premium will stay constant over the whole contract maturity, but the death and/or survival benefit (or the annuity payment) will increase by a fixed factor each year. This is typically to safeguard the benefit against inflation, so that the value of the annuity payment or death benefit does not diminish due to inflation. The initial contract valuation (i.e. the determination of the constant premium at contract inception) already takes into account that the benefits will not stay constant over the whole period, but increases by a constant factor each year. This type of yearly benefit increase by a fixed factor can be implemented by the parameters: * `annuityIncrease` ... The factor, by which potential annuity payments increase yearly. Default is 1.0 (no increase in annuity benefits) * `deathBenefit` ... The vector of death benefits (relative to the sum insured, which for endowments describes the survival benefit) In the following example, we create a 10-year endowment contract with constant premiums over the whole period and another one with idential parameters except that the premium increases by 4\% each year: ```{r FixedSumIncrease.WholeLife, results = "hide"} # For comparison: Contract with constant premiums contract.TermLife.Constant = InsuranceContract$new( tarif = Tarif.Life, sumInsured = 10000, age = 50, policyPeriod = 10, contractClosing = as.Date("2020-09-01") ) # Contract with 4% yearly increase in sum insured (final survival benefit is 10.000) contract.TermLife.SumInc = InsuranceContract$new( tarif = Tarif.Life, sumInsured = 10000, deathBenefit = (1.04)^(0:20), age = 50, policyPeriod = 10, contractClosing = as.Date("2020-09-01") ) premium.comparison = data.frame( `Const S.I.` = contract.TermLife.Constant$Values$absCashFlows[,"death"], `Const. Premium` = contract.TermLife.Constant$Values$absCashFlows[,"premiums_advance"], `4% sum increase` = contract.TermLife.SumInc$Values$absCashFlows[,"death"], `Premium w. sum increase` = contract.TermLife.SumInc$Values$absCashFlows[,"premiums_advance"], check.names = F ) premium.comparison ``` ```{r FixedSumIncrease.WholeLifeOut, results = "asis", echo=F} premium.comparison %>% pander ``` For annuities, the benefit increase is not handled through `deathBenefits`, but rather through the parameter * `annuityIncrease` ... yearly increase factor of the annuity payments In the following example, we create a 10-year endowment contract with constant premiums over the whole period and another one with idential parameters except that the premium increases by 4\% each year: ```{r FixedSumIncrease.Annuity, results = "hide"} # For comparison: Contract with constant annuity contract.Annuity.Constant = InsuranceContract$new( tarif = Tarif.DefAnnuity, sumInsured = 1200, age = 55, policyPeriod = 10, deferralPeriod = 5, premiumPeriod = 5, contractClosing = as.Date("2020-09-01") ) # Contract with 4% yearly increase in annuity benefits contract.Annuity.Increasing = InsuranceContract$new( tarif = Tarif.DefAnnuity, sumInsured = 1200, annuityIncrease = 1.04, age = 55, policyPeriod = 10, deferralPeriod = 5, premiumPeriod = 5, contractClosing = as.Date("2020-09-01") ) # Contract with 4% yearly increase in premiums and in annuity payments contract.Annuity.IncreasingBoth = InsuranceContract$new( tarif = Tarif.DefAnnuity, sumInsured = 1200, annuityIncrease = 1.04, premiumIncrease = 1.04, age = 55, policyPeriod = 10, deferralPeriod = 5, premiumPeriod = 5, contractClosing = as.Date("2020-09-01") ) premium.comparison = data.frame( `Const. Annuity` = contract.Annuity.Constant$Values$absCashFlows[,"survival_advance"], `Const. Premium` = contract.Annuity.Constant$Values$absCashFlows[,"premiums_advance"], `4% Annuity Increase` = contract.Annuity.Increasing$Values$absCashFlows[,"survival_advance"], `Premium w. Ann.Increase` = contract.Annuity.Increasing$Values$absCashFlows[,"premiums_advance"], `Inc.Premium w. Ann.Increase` = contract.Annuity.IncreasingBoth$Values$absCashFlows[,"premiums_advance"], check.names = F ) ``` ```{r FixedSumIncrease.AnnuityOut, results = "asis"} premium.comparison %>% pander ``` ## Dynamic Increases With dynamic increases, the contract initially is written with a fixed sum insured and constant premiums over the whole contract period. The future increases are not considered at all. After the initial contract inception, either yearly or when a consumer price index changes by a value larger than a given threshold, the sum insured is increased (either by a fixed amount or by an amount determined by the index change) and the premium is adjusted accordingly. Internally, the original contract is left untouched and the increase is modelled by a separate contract with the same key parameters, only with shorter duration and a sum insured that represents only the increase. The premium for this increase is calculated like a separate contract with only the difference in the over-all sum insured as its sum insured. Each dynamic increase then adds another separate tiny InsuranceContract object and the over-all values are the sums of all those contract blocks (sometimes also called "contract slices"). The `InsuranceContract` class provides a method to add a dynamic increase: * `InsuranceContract$addDynamics(t, NewSumInsured, SumInsuredDelta, id, ...)` Only one of `NewSumInsured` (new total sum insured) and `SumInsuredDelta` (only the difference between old and new sum insured) is needed. This method adds a new contract block to the given InsuranceContract, starting at time $t$ with `SumInsuredDelta` as its sum insured and its premium calculated from the shorter contract period and the sum insured delta. These blocks for dynamic increases are stored in the contract's `$blocks` list of children. The values stored in the contract are then simply the sum of all its children. Here is an example of a 10-year endowment, which has dynamic increases at times $t=5$, $t=7$ and $t=8$: ```{r DynamicIncrease.Endowment} # For comparison: Contract with constant annuity contract.Endowment.Dynamics = InsuranceContract$new( tarif = Tarif.Endowment, sumInsured = 10000, age = 40, policyPeriod = 10, contractClosing = as.Date("2020-09-01"), id = "Initial contract" )$ addDynamics(t = 5, NewSumInsured = 11000, id = "Dynamic at 5")$ addDynamics(t = 7, NewSumInsured = 12000, id = "Dynamic at 7")$ addDynamics(t = 8, NewSumInsured = 13500, id = "Dynamic at 8") # Over-all contract sum insured and premiums for all blocks combined contract.Endowment.Dynamics$Values$basicData[,c("SumInsured", "Premiums")] %>% pander ``` ```{r DynamicIncrease.EndowmentOut, results = "asis", echo = F} blk = c(list(`Over-all contract` = contract.Endowment.Dynamics), contract.Endowment.Dynamics$blocks) padArray = function(arr = NULL, pad = 0, len = 0) { padEnd = max(0, len - pad - NROW(arr)) # if len is too short, return an array containing at least the arr nrcols = ifelse(is.null(arr), 0, NCOL(arr)) rbind( array(0, dim = c(pad, nrcols)) %>% `colnames<-`(colnames(arr)), arr, array(0, dim = c(padEnd, nrcols)) %>% `colnames<-`(colnames(arr)) ) %>% `colnames<-`(colnames(arr)) } lapply(blk, function(b) { basic = padArray(b$Values$basicData, pad = b$Parameters$ContractData$blockStart) basic[,"SumInsured"] }) %>% bind_cols() %>% rowid_to_column("t") %>% mutate(t = t-1) %>% pander(caption = "Sum Insured for the over-all contract and each of the blocks") lapply(blk, function(b) { basic = padArray(b$Values$basicData, pad = b$Parameters$ContractData$blockStart) basic[,"Premiums"] }) %>% bind_cols() %>% rowid_to_column("t") %>% mutate(t = t-1) %>% pander(caption = "Premium time series for the over-all contract and each of the blocks") ``` # Profit participation In addition to the above guaranteed values, many contracts also include some kind of profit sharing. The total amount of money to be distributed is usually predetermined by law or regulation (in Austria by the ["Lebensversicherung-Gewinnbeteiligungsverordnung -- LV-GBV"](https://www.ris.bka.gv.at/GeltendeFassung.wxe?Abfrage=Bundesnormen&Gesetzesnummer=20009295), but the actual way they are distributed to individual contracts is up the insurance undertaking. The profit participation scheme defines profit participation allocations based on certain rates and bases, where the formulas and the types of profit are pre-determined in a formal document (which in this package will be implemented as an object of class `ProfitParticipation`), while the profit rates are determined by the management of the undertaking ("discretionary benefits"). Typical Austrian insurance contracts have one of two kinds of profit sharing mechanisms: * "Advance profit participation" or "direct contributions", which is a direct premium rebate, which can in theory be lowered or revoked at any time. * Yearly profit assignments into the reserves based on "total credited interet rate" and other profit attribution rates. ## Advance profit participation (premium rebate) To implement advance profit participation, one still needs to create a `ProfitParticipation` object, which can be empty. The contract parameters `advanceProfitParticipation` and `advanceProfitParticipationInclUnitCost` then define the premium rebate. They can be set either in the profit scheme or in the tariff definition. The latter is usually the easier way, as one profit scheme might be applicable to multiple different tariffs with different advance profit participation rates. As an example, we will use our `Tarif.Life` whole life tarif defined above and add a $38\%$ advance profit participation on the premium: ```{r AdvanceProfitExample} profit.Advance.V1 = ProfitParticipation$new( name = "Profit Scheme for advance profit participation, V 1.0", advanceProfitParticipation = 0.38 ); Tarif.Life.withPP = Tarif.Life$createModification( name = "Example Tariff - Whole/Term Life with profit sharing", tarif = "Life1PP", profitParticipationScheme = profit.Advance.V1 ) contract.LifePP = InsuranceContract$new( tarif = Tarif.Life.withPP, age = 40, policyPeriod = 10, sumInsured = 100000, contractClosing = as.Date("2019-09-01") ) ``` The premium composition shows that the profit participation ```{r advanceProfitExample.PremiumComposition, eval=F} contract.LifePP$Values$premiumComposition ``` ```{r advanceProfitExample.PremiumCompositionOUT, echo=F} contract.LifePP$Values$premiumComposition[,c("charged", "tax", "unitcosts", "profit.advance", "gross", "net")] %>% as.data.frame() %>% rowid_to_column("t") %>% mutate(t = t-1) %>% pander ``` ## The ProfitParticiption class The profit participation scheme of a tarif is represented by an object of the `ProfitParticipation` class. While the `InsuranceContract.Parameters` list contains elements for the profit rates, the implementation of the calculation of the profit parts is done by functions defined in the `ProfitParticipation` constructor. This scheme is passed to the `InsuranceTarif` or `InsuranceContract` via the `profitParticipationScheme` parameter. ` There are different types of profit participation assignments, based on the type of risks they are based upon: * __Interest profit__: total credited rate (minus guarantee) applied to some kind of reserve * __Risk profit__: risk profit rate applied to the risk premium, capital or the sum insured * __Expense profit__: expense profit rate applied to the sum insured * __Sum profit__: rate (depending on sum insured) applied to the sum insured * __Terminal bonus__: yearly attributions are collected and paid out only on contract maturity * __Terminal bonus fund__: Part of the ongoing profit allocation is not immediately attributed to the contract, but stored in a special reserve and paid out only on maturity. Each of these profit components in general depends in some way on a profit rate and a basis to which the rate is applied. So each component has the general functional form: $$Profit^{type}_t = Calc\left(Rate^{type}_t, Basis^{type}_t\right)$$ The most common calculation function is a simple multiplication, i.e. $Calc(r, b) = r\cdot b$, but other functions are possible, to The (default) parameters that can be passed to the `ProfitParticipation` constructor are: | | | |:-----|:----------| |**General profit setup** || | `waitingPeriod` | During the waiting period at the beginning of a contract, no profit participation is assigned | | `profitComponents` | describes the different components of profit participation ("interest", "risk", "expense", "sum", "terminal") | | `profitClass` | a profit ID used to identify the correct profit rates (rates are defined per profit class) | | `profitRates` | a data frame containing company-wide profit rates. Key columns are year and profitClass | |**Advance profit participation rates** || | `advanceProfitParticipation` | premium rebate (percentage discount on the gross premium) | | `advanceProfitParticipationInclUnitCost` | premium rebate (percentage discount on the gross premium including unit costs) | |**Regular profit participation rates** || | `guaranteedInterest` $i$ | Contract-specific override of the guaranteed intereste rate (only for profit participation purposes) | | `interestProfitRate` $ip_t$ | Profit interest rate (added to the guaranteed interest rate to arrive at the total credited rate) | | `totalInterest` $tcr_t$ | The total credited interest rate (sum of guaranteed interest and profit participation interest) | | `mortalityProfitRate` $rp_t$ | Mortality / risk profit rate | | `expenseProfitRate` $ep_t$ | Expenso profit rate | | `sumProfitRate` $sp_t$ | Sum profit rate (typically a function, depending on sum insured) | | `terminalBonusRate` $tb_t$ | Terminal bonus rate | | `terminalBonusFundRate` $tbf_t$ | Terminal bonus fund rate, i.e. which percentage of the assigned profits are withheld in a separate terminal bonus fund and only paid out at maturity. | For the calculation of the profit participation, the `ProfitParticipation` class holds a list of function pointers to calculate each component of profit participation, as outlined above. For each of interest, risk, expense, sum, terminal and terminal bonus fund the following three functions can be given: * **Profit rate**: return the profit rate as a function from the values of the contract Function signature: `function(rates, ...)` * **Profit base**: The quantity on which to apply the rate. Typically this function returns either the current reserve, the previous reserve (or some combination), the sum insured or the current risk premium. Function signature: `function(rates, params, values, ...)` * **Calculation**: A function taking the rate and the base and calculate the profit assigned for the specific profit component. Most common are a simple multiplication of base and rate, but other formulas are possible, too. Function signature: `function(base, rate, waiting, rates, params, values, ...) ` Thus, the constructor of the `ProfitParticipation` class also takes the following parameters: |Type of profit |Function for rate | Function for base | Function for calculation | |:-------------------|:--------------------------|:--------------------------|:----------------------------| |interest on accrued profit |`getInterestOnProfits` |- (existing profit) |- | |interest profit |`getInterestProfitRate` |`getInterestProfitBase` |`calculateInterestProfit` | |risk profit |`getRiskProfitRate` |`getRiskProfitBase` |`calculateRiskProfit` | |expense profit |`getExpenseProfitRate` |`getExpenseProfitBase` |`calculateExpenseProfit` | |sum profit |`getSumProfitRate` |`getSumProfitBase` |`calculateSumProfit` | |terminal bonus |`getTerminalBonusRate` |`getTerminalBonusBase` |`calculateTerminalBonus` | |terminal bonus fund |`getTerminalBonusFundRate` |`getTerminalBonusFundBase` |`calculateTerminalBonusFund` | In addition, the following parameters define functions for reserves: * `getTerminalBonusReserve` ... Calculate the reserve for the terminal bonus from the bonus assignments (old tariffs often use some kind of discounting or conditional reserving for the terminal bonus reserve) Function signature: `function(profits, rates, terminalBonus, terminalBonusAccount, params, values)` To calculate the actual benefits paid out from profit participation, the following parameters take the corresponding functions (signature: `function(profits, rates, params, values, ...)` ) | | | |:---|:---------| |`calculateSurvivalBenefit` |Benefit from profit participation at maturity (in addition to the guaranteed payout) | |`calculateDeathBenefitAccrued` |Benefit from profit participation upon death (in addition to the guaranteed payout) | |`calculateDeathBenefitTerminal` |Benefit from terminal bonus upon death (in addition to the guaranteed payout and regular profit participation) | |`calculateSurrenderBenefitAccrued` |Benefit from profit participation upon contract surrender (in addition to the surrender value) | |`calculateSurrenderBenefitTerminal` |Benefit from terminal bonus upon contract surrender (in addition to the surrender value and regular profit participation) | |`calculatePremiumWaiverBenefitAccrued` |Benefit from profit participation upon premium waiver (in addition to the surrender value) | |`calculatePremiumWaiverBenefitTerminal` |Benefit from terminal bonus upon premium waiver surrender (in addition to the surrender value and regular profit participation) | ### Existing functions to use While the details of a profit participation scheme are very specific and no two profit schemes are exactly alike, the basic functionality to extract rates and bases and the calculation functions are usually not so different. For this reason, the `LifeInsureR` package provides several little helper functions that provide the most common functionality for the definition of rates, bases and the profit calculation. See `?ProfitParticipationFunctions` for the full list. The most common functions are: * `PP.base.PreviousZillmerReserve(rates, params, values, ...)` * `PP.base.contractualReserve(rates, params, values, ...)` * `PP.base.previousContractualReserve(rates, params, values, ...)` * `PP.base.meanContractualReserve(rates, params, values, ...)` * `PP.base.ZillmerRiskPremium(rates, params, values, ...)` * `PP.base.sumInsured(rates, params, values, ...)` * `PP.base.totalProfitAssignment(res, ...)` * `PP.rate.interestProfit(rates, ...)` * `PP.rate.riskProfit(rates, ...)` * `PP.rate.expenseProfit(rates, ...)` * `PP.rate.sumProfit(rates, ...)` * `PP.rate.terminalBonus(rates, ...)` * `PP.rate.terminalBonusFund(rates, ...)` * `PP.rate.interestProfitPlusGuarantee(rates, ...)` * `PP.rate.totalInterest(rates, ...)` * `PP.calculate.RateOnBase(base, rate, waiting, rates, params, values, ...)` * `PP.calculate.RateOnBaseMin0(base, rate, waiting, rates, params, values, ...)` * `PP.calculate.RatePlusGuaranteeOnBase(base, rate, waiting, rates, params, values, ...)` * `PP.benefit.ProfitPlusTerminalBonusReserve(profits, ...)` * `PP.benefit.Profit(profits, ...)` * `PP.benefit.ProfitPlusGuaranteedInterest(profits, rates, ...)` * `PP.benefit.ProfitPlusTotalInterest(profits, rates, params, values)` * `PP.benefit.ProfitPlusHalfTotalInterest(profits, ...)` * `PP.benefit.ProfitPlusInterestMinGuaranteeTotal(profits, rates, ...)` * `PP.benefit.TerminalBonus5YearsProRata(profits, params, ...)` * `PP.benefit.TerminalBonus5Years(profits, params, ...)` * `PP.benefit.TerminalBonus(profits, params, ...)` ### Example profit scheme For example, imagine a tariff's total cumulated assigned profit $G_t$ and the benefits at time $t$ have the formulas: $$Prof_t = \left(G_{t-1} + TBF_{t-1}\right) \cdot \left(1 + i + ip_t\right) + ip_t \cdot \frac{\left(Res_{t-1} + Res_{t}\right)}{2} + rp_t \cdot P^r_t + ep_t \cdot SumInsured + sp_t(SI) \cdot SumInsured$$ $$G_t = G_{t-1} + (1 - tbf_t) \cdot Prof_t$$ $$TBF_t = TBF_{t-1} + tbf_t \cdot Prof_t$$ $$Death_t = G_t \cdot \left(1 + i + ip_t\right) + TBF_t$$ $$Matu_n = G_n + TBF_n$$ $$Surrender_t = G_t\cdot \left(1+\frac{i + ip_t}{2}\right) + 0.5 \cdot TBF_t$$ $$Red_t = G_t + 0.5 \cdot TBF_t$$ These formulas can be interpreted as following: * There are multiple profit components: interest profit, risk profit, expense profit and sum profit. * The total profit assignment $Prof_t$ in year $t$ consists of: * Interest (guarantee + interest profit rate) on the accrued profits and the terminal bonus fund * Interest profit on the average reserve * Risk profit as part of the risk premium (similar to advance profit participation, but on a year-by-year basis on the actual risk premium) * Expense profit relative to the sum insured * Sum profit relative to the sum insured. The rate depends on the sum insured, too (e.g. higher SI typically have higher sum profit) * Only a portion $(1-tbf_t)$ is added to the accrued bonus, while the rest ist stored in the Terminal Bonus Fund. * The existing cumulated profit $G_{t-1}$ and the terminal bonus fund $TBF_{t-1}$ yields interest with the guaranteed interest rate plus potentially the interest profit rate. If the total credited rate is lower than the guarantee, the guarantee is still applied to the existing profits. => `PP.rate.interestProfitPlusGuarantee` * Additionaly, interest profit is assigned with rate $ip_t$ (=0 if total credited rate is below guarantee) multiplied with the average of the current and the previous reserve for the guaranteed part. => `PP.base.meanContractualReserve` * The risk profit works similar to advance profit participation, only that part of the (actual) risk premium of the year is returned to the customer after having paid it. => `PP.base.ZillmerRiskPremium` * Expense profit is based on the sum insured, since most cost structures are linear in the sum insured and contain certain loadings, which are returned to the customer via this profit component. * A sum profit of $sp_t$ of the sum insured is added, even if no interest profit is distributed. The sum profit rate depends on the sum insured (as a function), since the charges expenses increase linearly in the sum insured while the actual cost do increase only sub-linear. So for higher sums typically more expenses are returned. => The sum profit rate will be a function rather than a single value each year. * Only a part $(1-tbf_t)$ of the profit assignment in year $t$ is added to the accrued profits $G_t$, while the rest $tbf_t$ is stored in the terminal bonus fund $TBF_t$, which is partially lost on surrender or premium waiver. The values of $Res_t$, $P^r_t$, $SumInsured$ and the guaranteed interest $i^g_t$ are prescribed by the tariff or contract, while the profit participation rates $ip_t$, $rp_t$, $ep_t$ and $sp_t$ are decided on a year-by-year basis by the management boards. The benefits for death, maturity, surrender and premium waivers are: * In case of death, the existing cumulated profits yield one additional year of interest => `PP.benefit.ProfitPlusInterestMinGuaranteeTotal` * At maturity of the contract, the existing cumulated profits are paid out in addition to the guaranteed benefits of the contract. => `PP.benefit.Profit` * In case of surrender (on average half a year after the contract's anniversary), half a year of interest is added to the existing cumulated profits from the last anniversary => `PP.benefit.ProfitPlusHalfInterestMinGuaranteeTotal` * When premiums are waived, the existing accrued profits are taken into account without any additional interest. => `PP.benefit.Profit` * The terminal bonus fund is paid out fully on death and at maturity, while half of the TBF is lost on surrender or premium waiver. This profit scheme can be easily be implemented as a `ProfitParticipation` object, where one can pass the functions for bases and calculation and also provide default profit rates: ```{r Example.ProfitParticipation} ProfitScheme.example = ProfitParticipation$new( name = "Example Profit Scheme, V 1.0", profitComponents = c("interest", "risk", "expense", "sum", "TBF"), getInterestOnProfits = PP.rate.interestProfitPlusGuarantee, getInterestProfitBase = PP.base.meanContractualReserve, getRiskProfitBase = PP.base.ZillmerRiskPremium, getExpenseProfitBase = PP.base.sumInsured, getSumProfitBase = PP.base.sumInsured, getTerminalBonusFundBase = PP.base.totalProfitAssignment, mortalityProfitRate = 0.15, expenseProfitRate = 0.01, sumProfitRate = function(params, ...) {if (params$ContractData$sumInsured > 1000000) 0.005 else 0;}, terminalBonusFundRate = 0.3, calculateSurvivalBenefit = PP.benefit.ProfitPlusTerminalBonusReserve, calculateDeathBenefitAccrued = PP.benefit.ProfitPlusInterestMinGuaranteeTotal, calculateDeathBenefitTerminal = PP.benefit.TerminalBonus, calculateSurrenderBenefitAccrued = PP.benefit.ProfitPlusHalfInterestMinGuaranteeTotal, calculateSurrenderBenefitTerminal = function(profits, ...) { profits[, "TBF"] / 2 }, calculatePremiumWaiverBenefitAccrued = PP.benefit.Profit, calculatePremiumWaiverBenefitTerminal = function(profits, ...) { profits[, "TBF"] / 2 }, profitClass = NULL ) ``` The calculation functions are not given, as they default to the correct `PP.calculate.RateOnBase` anyway. The interest profit rates are not given, as they will vary over time with no default value. Rathery, they need to be passed to the call to `InsuranceContract$addProfitScenario()` to calculate one particular profit scenario with given rates. In contrast, the mortality, expense and sum profit rates are initialized with some default values, which will be used if a profit scenario does not explicitly give those profit rates. ### Using the profit scheme in a tariff or contract The profit scheme defined above can now be used with the `profitParticipationScheme` parameter in a tariff or a contract. Usually, the profit scheme is a property of the product, so it should be specified in the tariff, but can be overridden in a contract. As an example, let us create an endowment contract with 500\% death benefit that uses this profit scheme: ```{r Example.PP.Endowment} contract.Endow.PP = InsuranceContract$new( tarif = Tarif.Endowment, sumInsured = 10000, deathBenefit = 5, age = 50, policyPeriod = 15, profitParticipationScheme = ProfitScheme.example, contractClosing = as.Date("2020-09-01") ) ``` In contrast to the guaranteed values, which can and will be calculated as soon as the contract is created, the profit participation needs to be explicitly called with the desired rates. A contract can store multiple different profit scenarios, which can be added with the `addProfitScenario()` method. This method can also be chained to add multiple scenarios (e.g. ) ```{r ExamplePP.Endowment.addScenario} contract.Endow.PP$ addProfitScenario(id = "Current total credited rate", guaranteedInterest = 0.005, interestProfitRate = 0.02, totalInterest = 0.025)$ addProfitScenario(id = "Current TCR-1%", guaranteedInterest = 0.005, interestProfitRate = 0.01, totalInterest = 0.015)$ addProfitScenario(id = "Current TCR+1%", guaranteedInterest = 0.005, interestProfitRate = 0.03, totalInterest = 0.035) ``` All profit scenarios are stored in the `InsuranceContract$Values$profitScenarios` list indexed with the id given in the call. The array containing all values of the profit scenario first holds the calculation basis and the rate for each of the profit components: ```{r ExamplePP.Endowment.Scenarios} contract.Endow.PP$Values$profitScenarios$`Current total credited rate` %>% as.data.frame() %>% select(ends_with("Base"), ends_with("Interest"), ends_with("Rate"), -TBFRate, -TBFBase, -totalInterest) %>% rowid_to_column("t") %>% mutate(t = t - 1) %>% kable() ``` The base for interest profit is the average of the reserve: ```{r ExPP.End.reserve, echo = F} contract.Endow.PP$Values$reserves %>% as.data.frame() %>% rownames_to_column("t") %>% select(t, SumInsured, Zillmer) %>% mutate(AvgZillmer = rollingmean(c(0,Zillmer))) %>% pander() ``` From the bases and rates, the calculation function (in our case simply the multiplication of rate and base) is applied to arrive at the yearly profit allocation of each component: ```{r ExamplePP.Endowment.ScenariosAttib} contract.Endow.PP$Values$profitScenarios$`Current total credited rate` %>% as.data.frame() %>% select(ends_with("Profit"), totalProfitAssignment, -totalProfit) %>% rowid_to_column("t") %>% mutate(t = t - 1) %>% pander ``` The `totalProfitAssignment` column is the sum of all component allocations in the given year (the $Prof_t$ in the formulas above). After all components are calculated, the yearly profit assignment is split into the part that is accrued in the regular bonus and the part that is placed in the terminal bonus fund, using the terminal bonus fund rate. Finally, the yearly regular and terminal bonus assignments can be summed up to the regular and the terminal bonus: ```{r ExamplePP.Endowment.ScenariosTBFTotal} contract.Endow.PP$Values$profitScenarios$`Current total credited rate` %>% as.data.frame() %>% select(TBFBase, TBFRate, TBFBonusAssignment, regularBonusAssignment, TBF, regularBonus, totalProfit) %>% rowid_to_column("t") %>% mutate(t = t - 1) %>% pander ``` The last step in the calculation of a scenario is to calculate the benefits for each of the possible types of payout: ```{r ExamplePP.Endowment.ScenariosBenefits} contract.Endow.PP$Values$profitScenarios$`Current total credited rate` %>% as.data.frame() %>% select(survival, deathAccrued, death, surrenderAccrued, surrender, premiumWaiverAccrued, premiumWaiver) %>% rowid_to_column("t") %>% mutate(t = t - 1) %>% pander ``` One can add as many profit scenarios as desired. Each of the rates in the `addProfitScenario`-call can also be a vector giving the corresponding rate for each year of the contract: ```{r ExamplePP.Endowment.Scenario.Decr} contract.Endow.PP$ addProfitScenario(id = "decreasing TCR", guaranteedInterest = 0.005, interestProfitRate = (15:0)/15 * 0.02, expenseProfitRate = c(rep(0.01, 5), rep(0.005, 5), rep(0, 6))) contract.Endow.PP$Values$profitScenarios$`decreasing TCR` %>% as.data.frame() %>% select(interestBase, expenseBase, interestProfitRate, expenseProfitRate, interestOnProfitRate, interestProfit, expenseProfit, totalProfit) %>% rowid_to_column("t") %>% mutate(t = t - 1) %>% kable ``` In the Excel export, a separate tab of the Excel file will hold all profit scenarios added to the contract. # Modifying the default calculation approach While the cash-flow approach described above and based on the `type` parameter of the `InsuranceTarif` works very well for all standart types of life insurance, sometimes a tariff does not follow the standard behaviour exactly. The valuation approach with all-determining cash flows is still correct, but the cash flows might need to be adjusted. For this, some hook functions are provided that allow modification of the contract's internals (e.g. cash flows) before all other calculations commence. Two hooks are provided, which allow modifications of the cash flow profiles before present values, premiums and reserves are calculated: | | | |:---|:-----| |`adjustCashFlows` | Adjust the premium and benefit cash flows of the contract | |`adjustCashFlowsCosts` | Adjust the cost cash flows | |`adjustPremiumCoefficients` | Adjust the coefficients of the premium calculation formulas | The function signature is `function(x, params, values, ...)`, where `x` is the object holding the standard cash flows determined for the contract. The return value of the hook function will be used instead of `x`. The \code{adjustPremiumCoefficients} hook has a slightly extended function signature \code{function(coeff, type, premiums, params, values, premiumCalculationTime)}. An example where the cash-flow-approach solely based on `type` does not immediately work is a waiting period of 3 years for the death benefit. In particular, if a person dies during the first 3 years of the contract, no death benefit is paid out. The easiest way to implement such cash flows is to let the `InsuranceTarif` first create the standard cash flows (with benefits during the first three years) and then provide a hook function that nullifies the benefits in the waiting period, before all present values, premiums and reserves are calculated. ```{r WaitingPeriod.Hook} contract.Endow.Waiting = InsuranceContract$new( tarif = Tarif.Endowment, sumInsured = 10000, age = 50, policyPeriod = 15, contractClosing = as.Date("2020-09-01"), adjustCashFlows = function(x, ...) { x[1:3, "death_SumInsured"] = 0; x } ) contract.Endow.Waiting$Values$cashFlows[,c("premiums_advance", "survival_advance", "death_SumInsured")] %>% pander contractGridPremium( axes = list(age = seq(20, 80, 10), adjustCashFlows = c(function(x, ...) x, function(x, ...) { x[1:3, "death_SumInsured"] = 0; x })), tarif = Tarif.Endowment, sumInsured = 10000, policyPeriod = 15, contractClosing = as.Date("2020-09-01") ) %>% `colnames<-`(c("Full benefit", "Waiting period")) ``` Another example are term-fixe insurances where the Zillmer premium also includes the administration (gamma) costs over the whole contract period. ```{r termfix.Zillmeradjust.Hook, eval=FALSE} costs = initializeCosts(alpha = 0.04, Zillmer = 0.035, gamma = 0.0015, gamma.fullcontract = 0.001), adjustPremiumCoefficients = function(coeff, type, premiums, params, values, premiumCalculationTime) { if (type == "Zillmer") { coeff[["SumInsured"]][["costs"]]["gamma", "SumInsured", "guaranteed"] = 1 } coeff }, ```