How to Improve Risk Management with Better Claims Data

As insurance carriers strive to achieve better financial results on their homeowners’ business line, it is critical that loss mitigation efforts are proactively managed as an enterprise initiative from both the claims and underwriting side of the business. Every potential claim that can be prevented or mitigated not only helps the carrier, but more importantly delivers a value to the policyholder.

Consider how the success and failure of a mission-critical predictive model-based optimization solution for the homeowners’ field inspection program depends on the quality of data points captured by claims adjusters. Setting up the right workflow to capture comprehensive data on the cause of loss, as well as thoroughly analyzing the severity and frequency of past claims that are associated with condition hazard issues, will help carriers appreciate the magnitude of the opportunity and the critical role a claims organization plays in enterprise risk management efforts.

The Business Problem

U.S. property/casualty insurance carriers spend approximately $200 million per year on homeowners’ inspection programs. Historically, carriers have invested proportionately little resources in optimizing inspection operations. In the past few years, however, as carriers return their strategic focus to profitability, many are looking at their inspection program and finding opportunity to drive financial value by optimizing their approach and by focusing on condition hazards.

In 2011, industry research by Marshall & Swift/Boeckh (MSB) revealed that approximately 30 percent of inspection dollars yield an actionable underwriting outcome. Essentially, 70 cents on every inspection dollar gets wasted. The majority of carriers are using random risk selection, simple home-grown guidelines or business rules engines to order field inspections.

Some forward-looking carriers, on the other hand, have developed predictive models that yield good results on predicting Insurance to Values (ITV) deviations and condition hazards. The carriers that recognized this problem, but have either abandoned the initiative or are currently struggling with the model’s performance have one factor in common: poor data quality. One of the key reasons the carriers failed in their predictive modeling efforts was the fact that critical claims information like cause of loss was not captured on every transaction, and data was not archived in a way that was fit for modeling efforts.

MSB has created a framework to help carriers benchmark themselves against different levels of inspection program maturity that is experienced across the industry. The MSB Inspection Optimization Maturity Model outlines the progression path of increasing effectiveness of a carrier’s inspection program.

The four dimensions that form the foundation of the assessment model are: Inspection Selection, Data Use, Workflow Integration, and Inspection Method. Each dimension includes steps of increasing sophistication up to a fully optimized approach. While each carrier’s approach is unique and may align to varying levels across each dimension, a carrier’s overall Inspection Maturity Level can be assessed by taking the average rating across all dimensions.

The ability to rank policies based on ITV and condition hazard risks, select the right set of polices to inspect, identify the most effective inspection method, then use an automated order and fulfillment process represent the optimized state of inspection management. Carriers who advance the maturity levels with the homeowners’ inspection program are transforming their inspection operations into a strategic profit center.

Over the next two years, it is predicted that a significant number of carriers will see inspection optimization as a key competitive differentiator and quickly mature their approaches to enjoy the significant financial and operational benefits.

Based on a recent survey (250 respondents) conducted by MSB, data showed that 79 percent of the homeowners insurance industry continues to have a significant upside opportunity in optimizing inspection processes.

The impact from proactively identifying and mitigating condition hazards is significantly higher than the returns from ITV premium uptick. A company’s predictive model’s ability to effectively identify and quantify condition hazards risks depends on good historical claims data.

Data Quality

Risk management is the art of clearly understanding and quantifying risk inherent in the portfolio and taking actions to manage the portfolio’s performance. The right blend of powerful analytic techniques and differentiated data can provide clarity to focus on a simple set of metrics and make right decisions the first time and every time. This promise heavily leans on the assumption that a carrier has good data. A well-designed claims data collection process not only has value in efficient claims settlement, but also downstream holds the key to strategic advantages in analytics, risk management, and pricing.