EFFECT OF ARTIFICIAL INTELLIGENCE ON RISK MANAGEMENT IN INSURANCE COMPANIES IN ABUJA

Abstract

Ideally, integrating Artificial Intelligence (AI) into insurance companies in Abuja was expected to substantially strengthen risk management practices. AI-enabled predictive analytics were projected to enhance underwriting precision, reduce information gaps between insurers and policyholders, and improve fraud detection systems. This study examined the effect of Artificial Intelligence on risk management in selected insurance companies in Abuja, Nigeria. The main objective was to determine how predictive analytics and automated decision-making systems influence risk identification and risk mitigation/control mechanisms. A cross-sectional survey research design was adopted. The population comprised 353 employees drawn from five selected insurance companies, from which a sample size of 187 was determined using Taro Yamane’s formula; however, 224 questionnaires were distributed to accommodate non-response, and 210 valid responses were retrieved using stratified sampling techniques. Primary data were collected through a structured five-point Likert scale questionnaire. Reliability was confirmed using Cronbach’s Alpha coefficients above 0.70 for all constructs. Data were analyzed using simple regression with SPSS version 27. The findings revealed that predictive analytics had a significant positive effect on risk identification (R² = 0.465, p < 0.001), while automated decision-making systems significantly influenced risk mitigation and control mechanisms (R² = 0.549, p < 0.001). The study concluded that AI-driven technologies significantly enhance risk management effectiveness in the Nigerian insurance sector. It was recommended that insurance firms increase investment in predictive analytics infrastructure and integrate automated decision-making systems into core risk management processes to improve operational efficiency and strengthen internal controls.