2024, Vol. 5, Issue 1, Part B

Bancassurance systems, representing the intersection of banking and insurance operations, have become critical to modern financial intermediation by providing diversified revenue streams and shared risk infrastructures. However, this integration introduces complex risk dynamics, particularly in predicting loan defaults where banking credit exposure interacts with insurance underwriting portfolios. Traditional univariate and logistic models often fail to capture the multifactorial and nonlinear relationships inherent in such hybrid systems.

This study develops and validates a multivariate statistical modeling framework that integrates discriminant analysis, logistic regression, and Bayesian inference to predict loan default risk within Bancassurance institutions. The research utilizes a dataset of 325,000 credit and insurance-linked transactions collected from Central Bank, IMF, and OECD Bancassurance databases spanning 2015 to 2023, covering institutions in the United States, United Kingdom, Nigeria, and Singapore. Model calibration and validation employed Monte Carlo cross-validation, variance inflation diagnostics, and Akaike Information Criteria (AIC) for model selection.

Findings reveal that incorporating multivariate interactions between credit exposure, insurance policy maturity, collateral adequacy, and macroeconomic volatility significantly improves predictive accuracy compared to baseline models. The optimal hybrid model achieved a 36.4% reduction in root mean square error (RMSE) and a 24.8% increase in true positive default detection compared to traditional logistic regression models. Furthermore, the Bayesian updating mechanism demonstrated adaptive learning capacity under regime shifts such as the COVID-19 credit disruption period.