Predictive Spending Data Gives Insurers New Insight Into Policyholder Risk

In a wide-ranging discussion, data experts Paul Hill and Marty Ellingsworth introduce a powerful new data model that redefines how insurers can assess financial stability and risk. By analyzing longitudinal spending and employment data, the model—developed from over a decade of workforce and household analytics—offers predictive insights into how consumers react under financial pressure. This is especially significant for insurance carriers facing unpredictable coverage lapses, rising default rates, and challenges in risk segmentation.

The "default trajectory model" segments households by income, age, and geography to reveal how different demographics respond to financial strain. For example, households with secure employment or skilled trades often maintain stable insurance coverage, while others are more likely to drop policies or reduce coverage when under pressure. This level of granularity helps insurers better understand and anticipate shifts in their books of business.

For claims adjusters, the implications are far-reaching. Knowing which customer segments are financially stable or at risk of coverage disruption can inform not only claims volume expectations but also identify potential cases where moral hazard or fraud risk may increase. Employment volatility, income compression, and inflation are not just underwriting issues—they are upstream indicators of claim behaviors and customer interactions.

This data-driven approach equips insurers with the foresight needed to address shifting consumer behavior proactively, making it a valuable tool for those managing risk, adjusting claims, or evaluating policyholder retention strategies.