Effects of Expected Credit Loss on Financial Performance of Listed Insurance Firms

Abstract

The purpose of the study was to examine the effects of expected credit loss on the financial performance of insurance firms. This study adopted a descriptive research design. The target population for this study comprised all six insurance firms listed on the Nairobi Securities Exchange (NSE) as of the year 2024. Descriptives and inferential statistics were used in data analysis. The analysis revealed there is a significant correlation between ECL and financial performance (ROA), where the correlation was r = 0.318 and the p-value was 0.005. This shows that improvements in the credit loss model help insurers to project exposures more accurately, hence reporting is more likely to project stable earnings and improved returns. In this case, the null hypothesis (H₁) is rejected as the test confirms the significance of ECL on forecasting and financial performance, thus the overall profitability of the firm.  The Expected Credit Loss (ECL) model demonstrated the positive correlation and predictive power, anchored in financial Intermediation theory, proved particularly useful in managing credit risks and enhancing predictability in investment portfolios. Its significant influence on ROA affirms that forward-looking credit risk modelling strengthens financial resilience and regulatory compliance, especially under IFRS 9 requirements. Operationalize Expected Credit Loss (ECL) with risk analytics integration. Insurers should embed ECL modelling within their investment and credit risk functions, incorporating tools like machine learning algorithms to forecast default scenarios and macroeconomic stressors. This is vital for compliance with IFRS 9 and will bolster financial resilience against credit shocks.