The Role of Data Analytics in Improving Fraud Detection in Nigerian Insurance Claims

This study examines the application of data analytics in detecting and deterring fraudulent insurance claims in Nigerian insurance companies. Insurance fraud, including staged accidents, inflated repair bills, fictitious claims, and arson for profit, is estimated to account for a significant proportion of total claims costs in Nigeria's non-life insurance market. The financial burden of fraud erodes insurer profitability, inflates premiums for honest policyholders, and undermines public confidence in the insurance system. This study adopts a mixed-method design, combining a survey of 85 claims managers and fraud investigators in general insurance companies operating in Lagos, Kano, and Port Harcourt with analysis of claims databases from three cooperating insurers. Data analytics techniques applied include outlier detection, network analysis for linked claim patterns, predictive scoring models, and text mining of claims narratives. Findings reveal that data-driven fraud detection identifies approximately three times as many suspicious claims as traditional investigator-led processes, with a false positive rate below 15 percent when combined with human review. Motor claims and fire claims exhibit the highest fraud probability scores. The study concludes that data analytics substantially improves the efficiency and accuracy of fraud detection in Nigerian insurance claims operations. It recommends that NAICOM establish an industry-level claims fraud database and mandate minimum data sharing standards to enable cross-insurer fraud pattern detection.

Keywords: data analytics, insurance fraud, claims management, predictive modelling, Nigerian insurance.