Although predictive modeling has been utilized by P&C insurers for years, the focus has primarily been on pricing and underwriting risks. We now are seeing increased interest and utilization of that process to manage claims. Current example applications of predictive modeling for claims include:

• Identifying potentially fraudulent claims
• Predicting which claims may benefit from having a specialist assigned to them
• Developing logic to more scientifically triage claims to the right level of expertise in the organization
• Determining what differences in the book of business are impacting claim severity

Process Can Reduce Errors

At P&C insurers and third party administrators, the claims management task is handled by teams of professionals consisting of claim adjusters, claim managers, nurse case managers, client risk mangers, and other claim and medical experts. While these professionals generally have years of experience and can identify claims that may need intervention, predictive modeling can help add discipline to this process and reduce the potential for human error by processing multivariate patterns that humans are incapable of recognizing.

At SRS, our current predictive modeling solutions rely on data from several areas: claimant demographics, employment data, injury details, medical billing information, and claim financials. We also use natural language processing to text mine and extract key information from claim handler notes, and then integrate this data into our models.