Prediction of IBNR claim counts by modelling the distribution of report lags
type: Item
instance of: scholarly article
title: Prediction of IBNR claim counts by modelling the distribution of report lags
zbMATH Open document ID: 0614.62134
DOI: 10.1016/0167-6687(87)90024-2
author: Kenneth S. Kaminsky
published in: Insurance Mathematics \& Economics
publication date: 1987-01-01T00:00:00Z
review text: Property/liability insurance companies write insurance to cover many possible events for many different kinds of insured groups. The company must, among other things, have a good idea of how many claims have yet to be reported on accidents which have already happened, and this must be done for each different kind of insured group. A claim which has been incurred (i.e., the accident has already happened) but not yet reported to the insurance company is called IBNR claim. In this paper, we discuss some methods of predicting the number of IBNR claims. These methods center on the principle of maximum likelihood prediction, and are based upon suitable assumptions about the nature of accident frequency and the delay (lag) between the accident and the report. A later paper will include simulations, examples with real data, and asymptotic results. A special feature of the present paper is that grouping of the data as it arises in insurance data collection is fully taken into account. An example illustrating the basic idea is presented at the end of the paper.
Mathematics Subject Classification ID: 62P05
zbMATH DE Number: 3994852
zbMATH Keywords: distribution of report lags
zbMATH Keywords: multinomial distribution
zbMATH Keywords: exponential distribution of lags
zbMATH Keywords: predicting the number of IBNR claims
zbMATH Keywords: maximum likelihood prediction
zbMATH Keywords: grouping
MaRDI profile type: Publication
full work available at URL: 0167-6687(87)90024-2
OpenAlex ID: W2065366609
cites work: Q4188569
cites work: Estimating Population Size with Exponential Failure
cites work: Estimation of IBNR claims by credibility theory
cites work: Q3314807
cites work: Maximum likelihood prediction
description: scientific article; zbMATH DE number 3994852