AI-Driven Business Analytics in Financial Risk Management: Key Insights from the Banking and Insurance Sectors

Purpose – This study investigates the role of AI-driven business analytics in strengthening financial risk management within the banking and insurance sectors. It examines the extent of AI adoption, its impact on organizational resilience, the challenges encountered during implementation, and the future prospects of AI integration in risk management practices. Design/methodology/approach – A quantitative research design was employed using a structured questionnaire administered to 350 professionals across banking and insurance organizations. The survey measured four core constructs—AI adoption, impact on risk management, challenges, and future prospects—using a five-point Likert scale. Descriptive statistics, reliability testing, and Pearson’s correlation analysis were conducted to ensure measurement validity and to examine interrelationships among constructs. Findings – Results indicate moderately high adoption of AI (M = 3.72, SD = 0.64) and a strong positive impact on risk management (M = 3.88, SD = 0.59). Respondents acknowledged moderate challenges (M = 3.41, SD = 0.70), including cost, regulatory uncertainty, and lack of skilled professionals. Despite these barriers, future prospects of AI in financial risk management were viewed with high optimism (M = 4.12, SD = 0.55). Correlation analysis confirmed significant positive associations between AI adoption, enhanced risk management, and future outlook, while challenges were negatively correlated with all other constructs. Originality/value – This study contributes to the growing body of knowledge on digital transformation in financial services by providing empirical insights into how AI-enabled analytics are reshaping risk management. The results indicate that the strategic role of AI in banking and insurance is not only significant but also that organizational, regulatory, and ethical issues are urgent in order to achieve the maximum benefit.