Journal of Finance and Economics
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Journal of Finance and Economics. 2021, 9(4), 130-139
DOI: 10.12691/jfe-9-4-2
Open AccessArticle

Risk of Fraudulent Claims and Financial Distress in Non-Life Insurance Companies in Kenya: A Structural Equation Modeling Approach

John Mose Michira1, Agnes Njeru1 and Florence Memba1,

1School of Business and Entrepreneurship, Department of Economics, Accounting and Finance, Jomo Kenyatta University of Agriculture and Technology, P.O. BOX 62, 000, 00200, Nairobi, Kenya

Pub. Date: July 18, 2021

Cite this paper:
John Mose Michira, Agnes Njeru and Florence Memba. Risk of Fraudulent Claims and Financial Distress in Non-Life Insurance Companies in Kenya: A Structural Equation Modeling Approach. Journal of Finance and Economics. 2021; 9(4):130-139. doi: 10.12691/jfe-9-4-2

Abstract

Financial distress (FD) is a common occurrence in Kenyan commercial sector and is not lacking in non-life insurance companies in Kenya. Several insurance companies have been placed under statutory management for failure to pay genuine claims and other creditors. Insurance companies provide unique financial services, not only to individuals but also to the growth and development of the economy; giving employment to workers and dividends to investors. Financial distress places insurable properties and businesses at risk thus reducing the general public confidence in the insurance sector. For this paper, the goal was to investigate whether fraudulent claims (FC) significantly cause financial distress in non-life insurance companies in Kenya. In accounting for insurance fraudulent claims, increases in fraudulent claims mean a reduction of profitability of an insurer; and payment of fraudulent claims drains the insurer’s cash flow, thus causing financial distress. Out of 37 non-life insurance companies, registered in 2018 in Kenya, four insurers were subjected to Pilot Testing and another four companies declined to participate in the survey. Secondary data from Insurance Regulatory Authority website was retrieved for calculations of Z-scores using Altman’s [1], amended formula. Using the discriminative Z-score formula, 52% of the non-life insurance companies in 2018 were financially distressed, compared to 48% in 2017. However, when considering the average of ten years (2009 to 2018), financially distressed companies were 38%. To confirm this distressful situation, primary data was also collected through a questionnaire. A partial least squares structural equation modelling (PLS-SEM) approach was employed to affirm the researcher’s hypotheses and further test whether theoretical framework was supported by primary data analysis. Goodness-of-fit (GoF) indices were used to assess the model’s goodness of fit. The structural path from FC to FD was found to be significant at 5% level of significance. Financial Distress (FD) increased with an increase in fraudulent claims (FC) (regression coefficient,  β= 0.32, 95% CI (0.16, 0.4)). This means that the relationship was significant in this study. In other words, for every unit increase in FC, FD significantly increased by 0.32. However, the indirect effect of FC on FD via IRA was not significant. Hence, IRA supervision was not a significant mediating factor. In a research in the USA by A. M. Best Company [2], alleged fraud in insurance claims was identified as one of contributors of insurance companies’ failure, accounting for 10%. An insurance fraud survey carried out by an audit firm KPMG [3] showed that Kenyans could have paid over Kshs 30 billion to cover for fraud. The researchers further observed that companies’ employees were found to be colluding with policyholders and claims agents to doctor and file illegitimate claims with the insurers. The insurance business classes which are most affected by fraud are motor and medical classes. The researchers recommends that members of staff of insurance companies be trained to effectively detect fraudulent claims; and that the insurance act be amended to give power to the board of directors in stamping out of financial distress in the insurance industry.

Keywords:
non-life insurance companies policyholders insurance regulatory authority claims reserving z-scores structural equation modelling

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