Artificial Intelligence and Predictive Analytics in Underwriting: Implications for Insurance Risk Assessment in Nigeria
Authors: Okeke, Daniel Chukwudi, Agbaji, B. C., Iloegbunam, D. N.
Journal: International Journal of Accounting and Financial Risk Management (IJAFRM), ISSN 3023-3690
Citation: IJAFRM 7(2): 1-13, 2026-03-07.
DOI: 10.70878/ijafrm.2026.e1480324
PDF: Download full-text PDF
Type: Original Research
Abstract
This study examined the common link between artificial intelligence and predictive analytics in underwriting and its implications for insurance risk assessment in Nigeria. The specific objectives were to: analyze the relationship between the adoption of artificial intelligence and fraud detection in underwriting and its implications in increasing efficiency and productivity in risk assessment; examine the relationship between the adoption of artificial intelligence and policy personalization in underwriting and its predictive implications in improving customer experience in risk assessment. A survey research design was adopted. The primary source of data was utilized for the study using a structured questionnaire as a data collection instrument. The T-test technique was adopted for testing the hypotheses. Findings revealed that; there was significant relationship between adoption of Artificial Intelligence (AI) and fraud detection in underwriting and it has substantial implication on efficiency and productivity in risk assessment (F = 1329.192, Pv
Keywords
Artificial Intelligence, Predictive Analysis, Underwriting, Insurance Risk Assessment, Fraud Detection, Policy Personalization, Customer Experience
Full Text
This study examined the common link between artificial intelligence and predictive analytics in underwriting and its implications for insurance risk assessment in Nigeria. The specific objectives were to: analyze the relationship between the adoption of artificial intelligence and fraud detection in underwriting and its implications in increasing efficiency and productivity in risk assessment; examine the relationship between the adoption of artificial intelligence and policy personalization in underwriting and its predictive implications in improving customer experience in risk assessment. A survey research design was adopted. The primary source of data was utilized for the study using a structured questionnaire as a data collection instrument. The T-test technique was adopted for testing the hypotheses. Findings revealed that; there was significant relationship between adoption of Artificial Intelligence (AI) and fraud detection in underwriting and it has substantial implication on efficiency and productivity in risk assessment (F = 1329.192, Pv