# PureIBNR

## 1.Introduction

This vignette will cover pure IBNR functions of the NetSimR package. It will overview the theory behind these functions, propose possible uses and illustrate examples. The reporting delay distributions supported are LogNormal and Gamma. An application of the pure IBNR functions is suggested in the article “Escaping the triangle” accessible here.

## 2.Backround theory

### 2.1 Reporting delay distribution

Reporting delay is the time difference between when the accident occured and when the insurer was notified. Reporting delay is a positive and positively skewed variable. It can me modelled with a LogNormal or Gamma regression. As suggested by the article cited in the introduction, estimating reserves can be more accurate if the reserves are split to pure IBNR and IBNER. Also note tha reporting delay is a right truncated variable. The closer the exposure period to the valuation date, the more the truncation. As such reporting delay would be better modelled by a right truncated regression.

### 2.2 From reporting delay to pure IBNR

Pure IBNR refers to claims not reported yet to the insurer, while IBNER refers to the over/under estimation in the outstanding part of reported claims. Together the two form the insurer’s reserves for claims occured. Unearned premium will also be calculated by the functions provided, and this relates to future claims from business already written. If we split the period to days, one can think of the reporting delay cumulative distribution function as the proportion of the day earned net of pure IBNR, based on the maximum reporting delay (Maximum reporting delay would be the difference between the reference day and the valuation day). As integration is equivalent to summing the function values, when the partition size is one, one can derive that integrating from the maximum to the minimum reporting delay calculates the pure IBNR earned exposure for the period. This can be proportioned with the full duration and the earned duration, to provide different metrics.

## 3. Functions usage

In this section we will suggest possible uses of the specified functions.

### 3.1 Pure IBNR

The function will input the inception, expiry, valuation dates and estimate the proportions unearned and allocated to pure IBNR. The pure IBNR proportion multiplied by the frequency-severity from exposure rating, can provide an accurate estimate of the reserve. Alternatively mutliplying this with the premium and a loss ratio proxy will provide an acceptable approximation of the reserve. The proportion of the unearned exposure multiplied by the premium will provide the unearned premium reserve. One can use the exposure net of unearned period and pure IBNR as the offset (exposure) for modelling frequency in exposure or experience rating. This will be the modellers choice rather than removing the last periods in situations where data is less. The pure IBNR and unearned exposures can be used for simulating stochastic claim reserves. Following the above, the analyst can incorporate the unearned and pure IBNR exposure models to dashboards, hence automate estimating the reserves. For all the above this method is able to drill down individual segments and be insensitive to portfolio mix changes, when the reporting delay is modelled with generalised linear models. When this method is combined with a truncated regression, the method can adjust for reporting delay development for the latest periods. The method can also reveal patterns otherwise invisible to the analyst. Reinsurance structures are easier to be implemneted on top of this method compared to triangles.

## 4. Examples

### 4.1 Pure IBNR

In the below example we will following these steps:

• Construct a policy dataset with policy inception/expiry dates
• Generate claims
• Generate accident and reporing dates, through a reporting delay disribution
• Derive reported claims up to the valuation date
• Model reporting delay
• Use the function of the NetSimR package to estimate PureIBNR duration
• Model claim frequency with adjusted claim duration and compare modelling without adjusting
• Predict claim frequency for pure IBNR and unearned duration and compare with our data
• Generate a chain ladder estimate for pure IBNR claims count and compare with the data and the GLM approach
• Simulate the pure IBNR reserve distribution
library("NetSimR")
#set the seed
set.seed(0)

#Set the reporting delay distribution parameters
mu <- 3
sigma <- 1

#Generate reporting delays and preview the distribution
x<-rlnorm(1000,mu,sigma)
summary(x)
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max.
##   0.7895   9.8902  18.9372  32.7484  39.9504 526.5859
hist(x, breaks = 100)

#Generate dates data
PoliciesPerDay<-100
PeriodLength<-365*2
InceptionDateStart<-as.Date("2011/1/1")
InceptionDate<-seq(InceptionDateStart, by = "day", length.out = PeriodLength)
InceptionDate<-rep(InceptionDate,PoliciesPerDay)
#assume all policies are yearly
DayPolicyDuration<-365
ExpiryDate<-InceptionDate+DayPolicyDuration
ValuationDate<-max(InceptionDate)
Data<-data.frame(InceptionDate,ExpiryDate)
summary(Data)
##  InceptionDate          ExpiryDate
##  Min.   :2011-01-01   Min.   :2012-01-01
##  1st Qu.:2011-07-02   1st Qu.:2012-07-01
##  Median :2011-12-31   Median :2012-12-30
##  Mean   :2011-12-31   Mean   :2012-12-30
##  3rd Qu.:2012-07-01   3rd Qu.:2013-07-01
##  Max.   :2012-12-30   Max.   :2013-12-30
ValuationDate
## [1] "2012-12-30"
#Generate Claims Counts Data and reporting delays
#Assume only 1 claim per policy possible with probability of claiming 10%
ClaimFreq<-0.1
Data$ClaimCount<-ifelse(runif(nrow(Data))<ClaimFreq,1,0) #generate accident date assuming uniformly happening throughout the year Data$AccidentDate<-Data$InceptionDate+runif(nrow(Data))*(Data$ExpiryDate-Data$InceptionDate) #generate reporting delays Data$ReportingDelay<-round(Data$ClaimCount*rlnorm(nrow(Data),mu,sigma),1) #generate reporting days Data$NotificationDate<-Data$AccidentDate+Data$ReportingDelay
#remove claims reported beyond valuation day
Data$ClaimReportedBeforeValuation<-ifelse(Data$NotificationDate>ValuationDate,0,Data$ClaimCount) #Maximum reporting delays Data$MaxReportingDelay<-as.numeric(ValuationDate-Data$AccidentDate)+1 #Earned Duration Data$EarnedDuration<-ifelse(Data$ExpiryDate>ValuationDate,as.numeric(ValuationDate-Data$InceptionDate),as.numeric(Data$ExpiryDate-Data$InceptionDate))/365

#Filter claims only data
ModelData<-Data[Data$ClaimReportedBeforeValuation==1,] #model reporting delays with a linear model lm<-lm(log(ModelData$ReportingDelay)~1)
#summary(lm)
cbind(coefficients(lm),summary(lm)$sigma) ## [,1] [,2] ## (Intercept) 2.916398 0.958557 #model reporting delays with a truncated model (splitting to monthly periods would be more accurate in terms of maximum reporting delay) library("crch") rTLm<-crch(log(ModelData$ReportingDelay)~1, truncated = TRUE, right = log(ModelData$MaxReportingDelay), dist = "gaussian", link.scale = "identity") #summary(rTLm) coefficients(rTLm) ## (Intercept) (scale)_(Intercept) ## 2.9752603 0.9919743 #Generate and predict pure IBNR and UPR Claims Data$PureIBNRClaims<-ifelse(Data$AccidentDate>ValuationDate,0,Data$ClaimCount-Data$ClaimReportedBeforeValuation) Data$UPRClaims<-Data$ClaimCount-Data$ClaimReportedBeforeValuation-Data$PureIBNR PureIBNRPrediction<-PureIBNRLNorm(Data$InceptionDate,Data$ExpiryDate,ValuationDate,coefficients(rTLm)[1],coefficients(rTLm)[2]) Data$PredictedPureIBNRYears<-PureIBNRPrediction$PureIBNRDuration/DayPolicyDuration Data$PredictedUPRYears<-PureIBNRPrediction$UnearnedDuration/DayPolicyDuration Data$NetEarnedDuration<-1-Data$PredictedUPRYears-Data$PredictedPureIBNRYears+0.00001

#Predict Claim Frequency with reduced exposure to adjust for pure IBNR
AdjFreqGLM<-glm(Data$ClaimReportedBeforeValuation~1, offset = log(Data$NetEarnedDuration), family = "poisson")
PredictedFrequency<-exp(AdjFreqGLM$coefficients) #Compare as if not adjusting for pure IBNR UnAdjFreqGLM<-glm(Data$ClaimReportedBeforeValuation~1, offset = log(Data$EarnedDuration+0.00001), family = "poisson") #summary(UnAdjFreqGLM) cbind(AdjustedModel=round(exp(AdjFreqGLM$coefficients),3),UnAdjustedModel=round(exp(UnAdjFreqGLM$coefficients),3),Actual=ClaimFreq) ## AdjustedModel UnAdjustedModel Actual ## (Intercept) 0.1 0.094 0.1 #Compare UPR Claim predictions cbind(Actual=sum(Data$UPRClaims),Predicted=round(sum(Data$PredictedUPRYears)*PredictedFrequency,0)) ## Actual Predicted ## (Intercept) 1888 1822 #Compare Pure IBNR Claim predictions cbind(Actual=sum(Data$PureIBNRClaims),Predicted=round(sum(Data$PredictedPureIBNRYears)*PredictedFrequency,0), Theoretical=327) ## Actual Predicted Theoretical ## (Intercept) 344 318 327 #Theoretical comes from re-running with the theoretical parameter values #Compare Pure IBNR predictions with Chain Ladder ClaimsAY1RY1<-sum(Data[Data$AccidentDate<as.Date("2012/1/1") & Data$NotificationDate<as.Date("2012/1/1"), ]$ClaimReportedBeforeValuation)

ClaimsAY1RY2<-sum(Data[Data$AccidentDate<as.Date("2012/1/1") & Data$NotificationDate<as.Date("2013/1/1"), ]$ClaimReportedBeforeValuation) ClaimsAY2RY1<-sum(Data$ClaimReportedBeforeValuation)-ClaimsAY1RY1-ClaimsAY1RY2

cbind(Actual=sum(Data$PureIBNRClaims),PredictedGLM=round(sum(Data$PredictedPureIBNRYears)*PredictedFrequency,0),PredictedCL=round(PredictedPureIBNRClaimsChainLadder,0))
##             Actual PredictedGLM PredictedCL
## (Intercept)    344          318         321
#Note: Chain ladder can predict better if periods brake down to months or quarters

#Sliced LogNormal-Pareto claim Severity assumption
mu=5.6
sigma=1.65
s=10000
alpha=1.5

#Simulate the reserve distribution
numberOfSimulations=10000
SimulatedYears <- numeric(length = numberOfSimulations)
ExpectedFrequency <- sum(Data\$PredictedPureIBNRYears)*PredictedFrequency

for (i in 1 :numberOfSimulations){
SimulatedYears[i]=round(sum(qSlicedLNormPareto(runif(rpois(1,ExpectedFrequency)),mu,sigma,s,alpha)),0)
}

#Visualising pure IBNR reserve distribution
head(SimulatedYears)
## [1]  389186  507867 1628801  236865  471060  437146
hist(SimulatedYears, breaks = 1000, xlim = c(0,1.5e6))

summary(SimulatedYears)
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max.
##   157777   293934   335702   373093   394701 25837662
#Note: the strength of the method illustrated is that it can have GLM factors for reporting delay, frequency and severity, and run the simulations per risk