Early Detection of Insurance Fraud: Integrating Temporal Patterns into Risk Stratification Models

Abstract

Insurance fraud carries hefty economic burdens worldwide, prompting insurers to create more advanced detection capabilities that can transcend the weaknesses of static, traditional red-flag systems. Though many risk variables have been studied with machine learning, the temporal aspect of claims—the timing of accident and claims submissions in respect to the onset of policy—is an area largely left unexamined but potentially high leverage predictive area. In this research, we examine the potential in temporal patterns as leading indicators in the early identification of automobile insurance fraud. My main goal here is to create and verify a powerful statistical model that systematically isolates and quantifies the predictive strength of early-reporting behavior while also controlling for a large suite of well-known risks. A hierarchical logistic modeling structure was used with a large sample size of 15,420 auto claims, including 923 confirmed instances of fraud. Demographic, policy, and accident variables were gradually added in successive models prior to the final inclusion of binary early-timed event (accidents and claims reported in the first 15 days after policy onset) indicators. The resulting final model showed excellent discrimination and achieved an Area Under the Receiver Operating Characteristic Curve (AUC) value of 0.800. We found that while policy characteristics (e.g., all-perils coverage), and accident conditions (policyholder at fault, OR=14.2), were the most salient predictors, early-reporting temporal patterns also were revealed to be directionally significant determinants of increased fraud risk. From an operational view, the model shows substantial efficiency benefits, with the model identifying a successful 85.8% of all fraudulent instances in the top quartile (40% rounded down) of claims sorted by the resulting risk score. The discovery points to the benefit in the incorporation of temporal analytics in the structure of detection programs in frauds, allowing the possibility in moving from reactive investigation processes in moving and moving more towards proactive, data-driven stratification in risks.