CREDIT RISK MODELLING TECHNIQUES FOR LIFE INSURERS

CREDIT RISK MODELLING TECHNIQUES FOR LIFE INSURERS

CHAPTER ONEINTRODUCTION 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 model-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 be confident 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.

Statement of the general problem

Credit risk for life insurers in Nigeria has generated a lot of misconceptions and misinterpretations as regards its importance, the best techniques in its modeling, its benefits to life insurers and most importantly in the socio-economic development of Nigeria.The confusion of methods to employ in reducing the risk involved with credits to live insurers both on the part of the insurers and the financial institution in question Credit availability to insurers has also been a very controversial issue as most insurers complain of not been assisted with credits.

Objectives of the study

The following are the aims and objectives of the study

To know the best techniques of credit risk modeling for life insurers. To examine the impact of credit risks on life insurers. To examine the benefits of credit to the life insurers. To examine the relationship between credit and performance of insurers. To know if credit facilities are readily made available to insurers.

Significance of the study

This study will be important to insurance companies in the management of credit risks when it comes to life insurers. This study also will be of importance to Nigerians in unraveling the importance of credit to their profitability. The study will be important to the government and insurance stakeholders on the best method of credit risk modeling techniques for life insurers. This study will be important to insurers in knowing the best method of repaying their loans or credits.

Scope and limitation of the study

This study is on the techniques of credit risk modeling for life insurers with the Nigerian insurance company serving as its case study.

Limitation of the studyFinancial constraint- Insufficient fund tends to impede the efficiency of the researcher in sourcing for the relevant materials, literature or information and in the process of data collection (internet, questionnaire and interview).Time constraint- The researcher will simultaneously engage in this study with other academic work. This consequently will cut down on the time devoted to the research work.

Research Questions

What are the best techniques of credit risk modeling for life insurers? What impact do credit risks have on insurance companies? What are the benefits of credit to the life insurer? What is the relationship between credit and the performance of insurers? Are credit facilities readily made available to insurers?

Research Hypotheses

Hypothesis 1 H0: credit risks negatively affect insurance/financial institutions. H1:credit risks positively affect insurance/financial institutions.Hypothesis 2 H0: credit risks taken by insurance/financial institutions are low. H1: credit risks taken by insurance/financial institutions are high.

Definition of terms

Credit risks: A 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. Model: a thing used as an example to follow or imitate. Insurance: an arrangement by which a company or the state undertakes to provide a guarantee of compensation for specified loss, damage, illness, or death in return for a payment of a specified premium. Life insurance: insurance that pays out a sum of money either on the death of the insured person or after a set period.

REFERENCE Abdul Kader, H., Adams, M.B. and Hardwick, P. (2010), The Cost Efficiency of Takaful Insurance Companies, Geneva Papers on Risk and Insurance: Issues and Practice, Vol. 35, No. 1, pp. 161-181. Adams, M.B and Buckle, M. (2003), The Determinants of Corporate Financial Performance in the Bermuda Insurance Market, Applied Financial Economics, Vol. 13, No. 2, pp.144-143. Adams, M.B., Hardwick, P. and Zou, H. (2008), Reinsurance and Corporate Taxation in the United Kingdom Life Insurance Industry, Journal of Banking and Finance, Vol. 32, No. 1, pp. 101-115. Akotey, O.J., Osei, K.A. and Gemegah, A. (2011), The Demand for Micro Insurance in Ghana, Journal of Risk Finance, Vol. 12, No. 3, pp. 182-194. Angove, J., and Tande N. (2011), A Business Case for Micro-Insurance: An Analysis of the Profitability of Micro-Insurance for Five Insurance Companies, Micro-Insurance Innovation Facility, International Labor Organization. Arellano, M. and Bond, S. (1991), Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations, Review of Economic Studies, Vol. 58, No. 2, pp. 277-297. Arrow, J.K (1963), Uncertainty and the Welfare Economics of Medical care, American Economic Review, Vol.53, No.5, pp.941-973. Baltagi, B.H. (2004), Panel Data: Theory and Applications, Physica-Verlag, Heidelberg. Blundell, R. and Bond, S. (1998), Initial Conditions and Moment Restrictions in Dynamic Panel Data Models, Journal of Econometrics, Vol. 87, No. 2, pp. 115-143. Bond, S.R. (2002), Dynamic Panel Data Models: A Guide to Micro Data Methods and Practice, Institute of Fiscal Studies, Cenmap Working Paper CWP09/02. Cargill, T.F. and Troxell, T.E. (1979), Modelling Life Insurance Savings: Some Methodological Issues, Journal of Risk and Insurance, Vol.46, No. 4, pp.391-410. Churchill, C. (2006), Protecting the Poor: A Microinsurance Compendium, International Labor Organization, Geneva, Switzerland. Churchill, C. (2007), Insuring the Low-Income Market: Challenges and Solutions for Commercial Insurers, Geneva Papers on Risk and Insurance: Issues and Practice, Vol. 32, No. 3, pp. 401-412. Churchill, C., Phillips, R.D., and Reinhard, D. (2011), Introduction to the 2011 Symposium Issue of JRI on Microinsurance, Journal of Risk and Insurance, Vol. 78, No. 1, pp. 1-5.

 

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