CHAPTER ONE

INTRODUCTION

1.1 Background to the Study

Insurance operates on the principle of risk pooling, whereby a large number of exposure units contribute premiums into a common fund from which the losses of the few unfortunate members are indemnified (Jemilohun, 2019). Motor insurance, being one of the classes of non-life insurance made compulsory by law in Nigeria, accounts for a substantial share of the premium and claims portfolio of the Nigerian insurance industry.

Data released by the National Insurance Commission indicate that the industry's incurred loss ratio for non-life business stood at about 41 per cent in 2022, having earlier stood at 45.0 per cent and 47.7 per cent in preceding periods (National Insurance Commission, 2023), a pattern that reflects the sensitivity of underwriting results to the claims experience of major classes of business such as motor insurance. Studies applying extreme value theory to large motor claims recorded in the Nigerian insurance market between 2013 and 2016 have shown that the frequency and severity of such claims can be modelled using a Negative Binomial-Generalised Pareto distribution, with useful implications for excess-of-loss reinsurance pricing (Chukwudum, 2019).

Rising costs of vehicle spare parts occasioned by import dependence and exchange rate depreciation, an expanding vehicle population, and continuing disagreements between insurers and claimants over the timeliness and adequacy of settlement (Ajemunigbohun & Oreshile, 2019) have combined to make the statistical behaviour of motor insurance claims a subject of continuing importance to underwriters, regulators and researchers alike. Given these dynamics, a rigorous statistical analysis of the pattern, distribution and determinants of motor insurance claims in Nigeria is required in order to support sound underwriting, pricing, reserving and reinsurance decisions within the industry.

1.2 Statement of the Problem

Despite the compulsory nature of motor insurance in Nigeria, the class continues to be characterised by rising claims costs, disputed settlements and a loss experience that makes it difficult for insurers to price policies adequately (Ajemunigbohun & Oreshile, 2019). Inadequate control of the claims process directly threatens the underwriting profitability and financial soundness of insurers offering motor cover. Yet many operators still depend on simplified, rule-of-thumb methods rather than rigorous statistical techniques in analysing and projecting motor insurance claims, leaving a gap between the sophistication of the underlying loss-generating process and the tools used to manage it. Where statistical analysis is undertaken, it is often restricted to a specific portfolio or a short time frame, so that a broader, updated statistical picture of motor insurance claims across the Nigerian market remains scarce. This study therefore sets out to examine the pattern, distribution and determinants of motor insurance claims in Nigeria, with a view to closing this gap in empirical knowledge and providing evidence to guide underwriting and reserving practice.

1.3 Objectives of the Study

The main objective of this study is to carry out a statistical analysis of motor insurance claims in Nigeria. The specific objectives are to:

i. examine the trend and pattern of motor insurance claims in Nigeria over the period under study;

ii. determine the statistical distribution that best describes the frequency and severity of motor insurance claims;

iii. evaluate the relationship between motor insurance claims and premium income (loss ratio);

iv. identify the factors that significantly influence the magnitude of motor insurance claims paid; and

v. recommend an appropriate statistical model for estimating and managing motor insurance claims in Nigeria.

1.4 Research Questions

The study is guided by the following research questions:

1. What is the trend and pattern of motor insurance claims in Nigeria over the period under review?

2. What statistical distribution best describes the frequency and severity of motor insurance claims?

3. What is the relationship between motor insurance claims and premium income (loss ratio)?

4. What factors significantly influence the amount of motor insurance claims paid by insurers?

5. Which statistical model is most appropriate for estimating motor insurance claims in Nigeria?

1.5 Research Hypotheses

The following null hypotheses are formulated to guide the study:

H01: Motor insurance claims in Nigeria show no statistically significant trend over the period under study.

H02: The frequency of motor insurance claims does not significantly follow a Poisson or negative binomial distribution.

H03: There is no statistically significant relationship between motor insurance claims and premium income.

H04: Selected factors (loss ratio, expense ratio and gross premium income) have no significant effect on the magnitude of motor insurance claims paid.

1.6 Significance of the Study

This study will be of benefit to insurance underwriters and actuaries, who will gain evidence-based insight into the pattern and drivers of motor insurance claims useful for pricing and reserving decisions. It will assist the National Insurance Commission and other regulators in formulating policies that promote solvency and fair claims practice within the motor insurance segment. Reinsurers will find the findings useful in setting retention levels and pricing excess-of-loss treaties, in line with the reinsurance-pricing relevance already demonstrated for large motor claims in Nigeria (Chukwudum, 2019). Policyholders stand to benefit indirectly through more accurately priced premiums and improved claims handling. Finally, the study will add to the relatively limited body of Nigerian literature on the statistical analysis of motor insurance claims and will serve as a reference for students and researchers in actuarial science, statistics and insurance.

1.7 Scope and Limitation of the Study

The study covers the statistical analysis of motor insurance claims within the Nigerian insurance industry, drawing on secondary data obtained from the National Insurance Commission and/or selected insurance companies over a defined period of years. The analysis is limited to descriptive statistics and standard claims-modelling techniques (such as frequency-severity and regression-based approaches) and does not extend to other classes of insurance business. The study is limited by the availability and quality of secondary claims data, possible inconsistencies in industry reporting, and the fact that findings based on the selected period and sample may not fully capture emerging trends outside the period studied.

1.8 Definition of Terms

Insurance: A contractual arrangement in which an insurer agrees, in exchange for a premium, to indemnify the insured against specified losses.

Motor Insurance: A class of general insurance that provides financial protection against loss or damage arising from the use of motor vehicles.

Third-Party Insurance: The minimum, legally compulsory form of motor insurance, covering the insured's liability to third parties.

Comprehensive Insurance: A motor insurance policy that covers third-party liability as well as loss of or damage to the insured's own vehicle.

Premium: The amount paid by a policyholder to an insurer in consideration for insurance cover.

Claim: A formal request made by a policyholder to an insurer for compensation following the occurrence of an insured event.

Claims Frequency: The number of claims arising from a given portfolio of policies within a specified period.

Claims Severity: The average monetary size of claims arising from a portfolio of policies.

Loss Ratio: The ratio of incurred claims to earned premium, used to assess the underwriting performance of an insurer.

Underwriting: The process by which an insurer evaluates and accepts risk in exchange for premium.

Reinsurance: An arrangement whereby an insurer transfers part of its risk to another insurer (the reinsurer) in exchange for a share of premium.

Statistical Distribution: A mathematical function describing the likelihood of different outcomes, such as claim counts or amounts, in a data set.

REFERENCES

Ajemunigbohun, S. S., & Oreshile, S. A. (2019). Risk aversion and motor insurance demand: Empirical evidence from Nigeria. Annals of the University of Craiova, Economic Sciences Series, 2(47), 211–222.

Chukwudum, Q. C. (2019). Reinsurance pricing of large motor insurance claims in Nigeria: An extreme value analysis. International Journal of Statistics and Probability, 8(4), 1–15. https://doi.org/10.5539/ijsp.v8n4p1

Jemilohun, V. G. (2019). Statistical analysis of insurance claims reserves in Nigeria. International Journal of Pure and Applied Sciences and Technology, 41(1), 1–10.

Klugman, S. A., Panjer, H. H., & Willmot, G. E. (2019). Loss models: From data to decisions (5th ed.). Wiley.

National Insurance Commission. (2023). Annual statistical market report 2022. NAICOM.

Rejda, G. E., & McNamara, M. J. (2021). Principles of risk management and insurance (14th ed.). Pearson.