Disability

Traditionally, disability has been treated as an expense in the insurance world. From that comes the overall directive to management: focus on keeping expenses down. Predictive analytics has changed that. Predictive analytics can find opportunity in other ways:

  • By using claim scoring to identify claims that show excellent opportunity for recovery.
  • By using claim scoring to identify claims that show opportunity for offset.
  • By quantifying the relationships between risk factors and claim costs, so as to better predict the expected cost of writing a policy.

Claim Opportunity Reports

Innovation at Claim Analytics is not limited to modeling. Our success is tied to that of our clients and we work closely with them to ensure their business processes make full use of the information our models provide.

Claim Opportunity reports are an example of how our creativity enhances our clients’ business processes. These reports highlight a small percentage of active disability claims where the claims management team can have the greatest impact by working towards specific claim outcomes.

Pricing

A predictive model can include more factors in its morbidity calculations than traditional methods can manage. Furthermore, predictive modeling offers advantages in quantifying the inter-relationships between factors. The result is more a more accurate measure of risk, leading to a more accurate underwriting assessment and greater (and known level of) confidence in this assessment.

The improved accuracy of underwriting can be used to strategize the pricing, being more aggressive when you know the actual cost of the policy is lower than what traditional methods would suggest, or pricing higher when they know the cost is higher than traditional methods would suggest or when there is a lower level of confidence.

Rehabilitation

Claim Analytics predictive modeling tools can be used to analyze the impact of rehab referrals on claim outcomes in both STD and LTD. The objective of this is to identify where rehab intervention has had a positive, neutral or negative impact on claim outcomes, both overall and by various factors such as diagnosis, spend and timing.  This can be an effective tool to help carriers realize the greatest impact from their rehab investment.    

A key feature of the analysis is the expected recovery rate calculated for each claimant. These expected recovery rates reflect the unique characteristics of each claimant (e.g., age, elimination period, diagnosis, etc.) and are based your own claimant recovery experience. Our analysis compares the actual outcome (recovery or not) to the expected outcome for each claim that had a rehab referral. This allows us to analyze and summarize the impact of rehab intervention by the various claimant characteristics.  

This can be extended to focus on a proactive and automated scoring based approach to identifying claims to refer to rehab.  Based on what is learned from the rehab study, a custom claim scoring mechanism can be developed to identify claims that are likely to have improved outcomes with rehab intervention.  The scoring mechanism would typically run daily or weekly.

Fraud and Abuse Prevention

Provider fraud and abuse is an area many insurers can save a significant amount of money. Our provider fraud models have effectively identified fraudulent provider activities that were not known in the past as they are trained to identify service providers with atypical treatment practices. This approach will result in more false positives – think of a dentist that specializes in patients with dental phobia. However, by identifying those providers whose practices differ from their peer group this modeling approach can uncover fraudulent and abusive practitioners and uncover unknown and novel schemes.

Claim fraud largely relies on individual investigation. We can simplify this by flagging suspicious claims by using a predictive model trained to identify atypical claim activity.