Nigeria’s insurance sector has undergone rapid transformation in recent years, driven by digital innovations, regulatory reforms, and increasing macroeconomic volatility. Yet, persistent credit exposure across underwriting portfolios and investment operations continues to threaten the sector’s solvency and performance stability. Traditional deterministic approaches to credit risk assessment often fail to capture the inherent randomness, tail dependencies, and correlated shocks affecting insurer portfolios in emerging economies. This study develops and validates a stochastic modeling framework for assessing and predicting credit risk in Nigeria’s insurance industry.
Using a dataset of 150,000 policy-linked credit transactions and corporate bond exposures from 2010 to 2023, sourced from the National Insurance Commission (NAICOM), Central Bank of Nigeria (CBN), and IMF Financial Soundness Indicators, the study integrates three stochastic methodologies, Monte Carlo Simulation, Markov Chain transition matrices, and Stochastic Differential Equations (SDEs), to model credit risk volatility and default probabilities. The model is calibrated using maximum likelihood estimation and validated through bootstrap resampling and Akaike Information Criterion (AIC) metrics.
Findings indicate that stochastic processes outperform conventional static models in capturing cyclical default behavior and volatility clustering in Nigeria’s insurance portfolios. The optimal stochastic framework achieved a 31.8% reduction in prediction error and improved Value-at-Risk (VaR) estimation accuracy by 27.5% relative to deterministic models. Moreover, the Markov Chain component effectively captured regime shifts between credit stability and distress states during macroeconomic shocks, such as the 2016 recession and the COVID-19 period.
The study underscores the value of stochastic modeling in enhancing predictive accuracy, regulatory compliance, and capital adequacy management within Nigeria’s insurance industry. It concludes by recommending the integration of stochastic analytics into the NAICOM solvency assessment framework and insurer enterprise risk management (ERM) systems, ensuring resilience against systemic credit shocks in a volatile economic environment.
International Journal of Multidisciplinary Trends
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