Does Claims BI Just Mean “Bodily Injury?”

Anyone in the insurance claims industry that works on BI is not talking about Business Intelligence. Rarely is BI ever applied in insurance claims to mean business intelligence because most carriers only use business intelligence generically to examine closure rates, expense payments, and contact rates. Business intelligence is most often used primarily to analyze data in other business units like agent performance, product profitability and policy discounts.

By properly applying business intelligence and measuring analytics in the claim handling process, carriers have the opportunity to review and grade adjusters for improvement and development of claim adjudication best practices. Monitoring and reviewing claim handling practices will ensure adjusters are performing quality investigations resulting in fair and proper claim settlements for the carrier and the insureds.

A claim is the core of why people purchase insurance products – to get reimbursed when they incur a loss. A claim becomes a personal touch point with the insured, as well as a prospective insured when third parties are involved. How many carriers have used claimants switching to them after a claim to advertise their service? Leveraging analytics to generate business intelligence on claims processes, insured retention, and claimant satisfaction, as well as measuring things like allocated loss expenses, the number of claimants with attorneys, and post closure actions, can be used more directly and efficiently to impact the success of claims handling.

Of course you may not want all of your insureds since there are those that are working to use insurance claims to make money. Properly applied analytics and techniques can detect patterns and trends in claim participation, injuries, supplemental repairs, etc. I know of one specific case where analytics found that a claimant was paid five times for a single leg amputation, and another where a doctor was treating an average of 1,600 patients per day. Business intelligence can also capture the effectiveness of independent medical exams on claim settlements, better understanding and control on reserves, back to work rates, and therapies to move claimants from total disability to partial disability.

The next logical step is moving into predictive modeling.  Properly applied claims analytics helped one western insurer realize their return on investment in a matter of months, when they could proactively augment and deploy needed field staff to respond to several catastrophic storms.

By improving best practices, identifying fraud early, and employing predictive modeling, not only will customer satisfaction be effected, but this will also trigger claims closing more quickly and at lower costs, increasing the number of claim files adjusters can handle and lowering loss ratios. In this tough economy, lowering loss ratios by even as little as 1% can have a big impact on a company’s bottom line.

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