Student Bio
Lawrence Kareigi is a masters student at the University of Nairobi specializing in Actuarial Science. He works as an Actuarial Specialist at Heritage Insurance Company Kenya Ltd. He has accumulated over seven years of work experience spanning actuarial work, underwriting, banking and teaching. His research focuses on the application of Bayesian methods in predicting claims for non-life insurers. He holds a BSc. Actuarial Science degree from Jomo Kenyatta University of Agriculture and Technology (JKUAT) having graduated with First Class Honours. He possesses vast analytical skills with bias applications in general insurance modelling and data analytics.
Project Summary
Project Title: Claims Reserving for Non-life Insurers using a Bayesian Approach
Abstract
Claims reserving is a major role for actuaries in the general insurance industry. A deficiency in the level of the reserves could lead to a company failing to honour its obligations and even lead to insolvency. It is crucial that the methods used for calculating reserves be as accurate as possible in predicting the expected claims for an insurer. The estimation of claim reserves by actuaries revolves around incurred but not reported claims. There are different methods available for this estimation, broadly grouped into deterministic and stochastic methods. The Basic Chain Ladder method, a deterministic method, is compared to the Mack model, a stochastic model. The main advantage of the latter is that it has additional measures of precision of reserve estimates and can be used to determine the possible standard error associated with the model fit. Finally, a Bayesian approach to loss reserving is modelled by considering a growth curve for the claims development. This framework allows for additional information, not present in the data to be included in the model development. The model resulting from a Bayesian approach entails a predictive distribution of possible reserve estimates. The computational difficulties associated with using Bayesian methods are made easier to deal with using Markov Chain Monte Carlo techniques. The prediction power of all models are compared in order to determine whether the use of more complex models leads to improved reserve estimates.