Wouldn't it be nice to...

• know the outcome of a claim on the day it was reported?
• provide accurate, deserved, and timely benefits to an injured worker?
• assign a claim to an adjuster who can effectively and efficiently handle it, thus positively impacting client loss ratios as well as your internal claim leakage?

These are all seeds that can grow to impact claim outcomes, but are they the seeds of weeds or flowers?

 

For the past decade, the casualty market has been using predictive modeling in underwriting as a basis for effective segmentation of risks and for pricing guidance in renewal/non-renewal decisions. 

 

Effective application of a basic underwriting predictive model can positively impact a workers' compensation loss ratio by 5% to 10%.  In the underwriting modeling scenario, the model takes a snapshot of a given risk, typically 90 days before renewal. That snapshot is an annual prediction of a loss ratio given all the appropriate variables at hand.

 

In the area of workers comp claims, predictive modeling is not as easy or straightforward. Though similar in concept, a worker's comp claims model attempts to predict a snapshot of loss ratio on a weekly or even daily basis. This fact alone adds multiple layers of complexity to the development and implementation of a model.  It also makes it more difficult to understand the loss improvement in terms of earlier return to work.  This implies better use of a medical network, better case-management expertise and better decisions by doctors, adjusters, employers, and the injured person. 

 

A workers comp claim model can provide value to all parties and result in better outcomes for thousands of injured employees.  Employers and carriers will see an immediate impact on loss ratios and carriers will have the added benefit of better control of their unallocated loss adjustment expenses, including salaries, overhead, and other related adjustment costs not specifically allocated to the expense incurred for a particular claim. 

 

Predictive models can identify cost drivers and other factors influencing the outcome of claims and produce actionable information users can employ to produce savings on many levels.   

1.        Claims Complexity Model - identifies the primary drivers of a particular claim and affords the greatest reach into understanding a claimant's prospective outcome.

2.        Fraud-Only Model - identifies certain types of fraudulent behavior.  Is excellent for understanding and examining provider payment patterns.

3.        Subrogation-Only Model - identifies additional cases that may be subject to hidden recovery-based information.

4.        Nurse Case Management Model - identifies the right time to assign a nurse case manager. Proper timing can positively impact return-to-work and medical costs.

5.        Medical Reserve Model - identifies escalating drivers of medical costs and helps predict the outstanding costs.

6.       Catastrophic Chronic Pain - identifies which cases will cross the threshold of catastrophic chronic pain, offering more opportunity for management and timely notification.

You are invited to join the Clearinghouse effort and benefit from our Analytics for All approach from Benchmarking to KPIs and KPPs to Claim Scoring.