Behavioral Shifts in Digital Insurance Risk Assessment

Abstract

Digital insurance platforms increasingly rely on automated scoring systems to evaluate policyholder risk. Over time, changes in customer behavior, claim submission practices, and fraud strategies alter the statistical properties of incoming data streams. This study analyzes claim and policy records collected over a six-year period from a large online insurance provider, comprising approximately 860,000 active policies and 120,000 reported incidents. Temporal analysis reveals gradual shifts in reporting latency, repair cost distributions, and documentation completeness following the introduction of mobile claim applications. A dynamic recalibration procedure based on rolling feature normalization and adaptive threshold control was implemented. After adoption, false-positive fraud alerts decreased by nearly 14%, while early detection of high-risk cases improved. Sustaining assessment accuracy requires continuous adaptation rather than periodic model replacement.