Purpose This paper aims to measure the extent of effects of insurance fraud on the financial performance of insurance companies in Ghana. It also examines the causes and stringent measures that can be used to fight against insurance fraud. Design/methodology/approach Primary and secondary data obtained from 39 insurers in Ghana are used in this paper. A multiple regression model is used to determine the relationship between financial performance and insurance fraud variables. Findings The results from the model indicate that statistically insurance fraud has a significant negative effect on the annual return on assets (financial performance) of insurers in Ghana. Also, weak internal controls, poor remuneration of employees, falsified documents, deliberate acts of policyholders to profit from the insurance contract and inadequate training for independent brokers are found to be the major causes of insurance fraud in Ghana. To deter insurance fraud, effective internal fraud policy, rigorous assessment of insurance policies and claims, adequate training for independent brokers on insurance fraud and modern information technology tools are paramount in fighting this menace in Ghana. Research limitations/implications These findings are to have substantial impact on the techniques insurance companies will develop to fight insurance fraud and the policies that will be developed by governments and national insurance regulatory bodies to fight this menace. Originality/value The main value of this paper is the determination of the key variables that constitute insurance fraud and their impacts on the annual financial performance of insurance companies in Ghana.

The need for stochastic asset models has evolved from a common global standard for risk management in the Solvency II regime in Europe, IAIS Common Principles, Global ORSA standards NAIC, EIOPA, and OSFI. But the challenges in developing markets such as; lack of good quality data, inconsistent data coverage, market data not having long enough history, and lack of liquidity in certain parts of asset market have caused the absence of such models in Ghana. There have been a number of actuarial stochastic asset models designed for simulating future economic and investment conditions in several parts of the world. This study has discussed three of such models and determined which best fits the Ghanaian economic data. The data used for the empirical analysis in this study were taken from the Bank of Ghana database and the Ghana Stock Exchange. The study re-calibrated the models to derive the parameter set then compared the model results numerically after running 10000 simulations for 50 horizons. Investigations about the basic statistics of the simulated results for all the models are compared. The analysis revealed that all of the Ghanaian investment series used in the stochastic investment modeling are non-stationary in their mean, variance and auto-covariance. The study then found that the "Wilkie linear model" produced simulated values with similar characteristics to the historical data whiles the Whitten & Thomas TAR model produced simulated values with minimal forecast error. The study therefore suggests that since the "Wilkie linear model" has a relatively better parsimony, ready economic interpretation and its ability to mimic some important features of the Ghanaian economic series it deserves the attention of the actuary seeking to model jointly the behavior of asset returns and economic variables that matter in economic capital determination of insurance and pension business in Ghana.

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