Credit Risk Modelling Techniques for Life Insurers

Credit Risk Modelling Techniques for Life Insurers

Chapter One of Credit Risk Modelling Techniques for Life Insurers

INTRODUCTION

Background of the study

This study examines the factors that influence the techniques of credit risk modeling for life insurers in Nigeria – a major developing economy of sub-Sahara Africa. Credit risk is the risk of default on a debt that may arise from a borrower failing to make required payments.In the first resort, the risk is that of the lender and includes lost principal and interest, disruption to cash flows, and increased collection costs. The loss may be complete or partial and can arise in a number of circumstances

Life insurance provides risk protection for low income earners and is part of the growing international micro-finance industry that emerged in the 1970s (Churchill, 2006, 2007; Roth, McCord and Liber, 2007; Matul, McCord, Phily and Harms, 2010). Approximately, 135 million people worldwide currently hold life-insurance policies with annual rates of growth in some emerging markets estimated to be up to 10% per annum (Lloyd’s of London, 2009). However, this number of life-insurance policies represents only about 2% to 3% of the potential market (Swiss Re, 2010 p.9). By protecting low income groups from the vulnerability of loss and shocks, life-insurance is increasingly being spouted as a formalized risk management solution to world poverty and a key driver of economic growth and entrepreneurial development in low income countries such as those of west Africa (Churchill, Phillips and Reinhard, 2011).

Over the last decade, a number of the world’s major banks have developed sophisticated systems to quantify and aggregate credit risk across geographical and product lines. The initial interest in credit risk models stemmed from the desire to develop more rigorous quantitative estimates of the amount of economic capital needed to support a bank’s risktaking activities. As the outputs of credit risk models have assumed an increasingly large role in the risk management processes of large banking institutions, the issue of their potential applicability for supervisory and regulatory purposes has also gained prominence. This review highlighted the wide range of practices both in the methodology used to develop the models and in the internal applications of the models’ output.

This exercise also underscored a number of challenges and limitations to current modeling practices. From a supervisory perspective, the development of modeling methodology and the consequent improvements in the rigor and consistency of credit risk measurement hold significant appeal. These improvements in risk management may, according to national discretion, be acknowledged in supervisors’ assessment of banks’ internal controls and risk management practices. From a regulatory perspective, the flexibility of models in responding to changes in the economic environment and innovations in financial products may reduce the incentive for banks to engage in regulatory capital arbitrage. Furthermore, a models-based approach may also bring capital requirements into closer alignment with the perceived riskiness of underlying assets, and may produce estimates of credit risk that better reflect the composition of each bank’s portfolio. However, before a portfolio modeling approach could be used in the formal process of setting regulatory capital requirements, regulators would have to beconfident that models are not only well integrated with banks’ day-to-day credit risk management, but are also conceptually sound, empirically validated, and produce capital requirements that are comparable across institutions.