Why is claims management a perfect fit for EDM? Well, it is key for customers and agents to have a positive experience when first providing details of a claim as this sets the tone for the whole interaction. This tends to push companies to capture the minimum amount of detail possible to keep forms short and sweet. To automate the process of payment, however, a company needs to collect as much data as possible and verify its accuracy. One way business rules can help resolve this conflict is by driving the data collection process based on data entered so as to ask the minimum number of questions, given the specifics of the claim, while still collecting enough data to actually process it. For example, processing claims for home insurance in California can be very different from processing claims from other parts of the country due to the incidence of earthquakes. In Florida one must consider hurricanes and so on. A rules-driven approach can allow the claims application to ask additional questions about the specific circumstances of the claim using smart forms.
Once the right data has been collected the claims can also be routed appropriately based on additional rules, referring complex claims rapidly to an experienced adjuster and feeding others into a more automated system.
One of the key issues in claims is to quickly identify fraudulent claims while not annoying honest claimants with unnecessary delays and problems. A company must not pay too soon and have to re-coup losses later nor must in delay legitimate payments. This is an ideal situation for rules and analytics to work hand in hand. Predictive models can be developed that estimate the likelihood of fraud initially and as additional data is collected about the claim. Combined with rules to manage acceptable levels of risk for automatic payment or referral to special investigators these models allow for very efficient process automation. In the gray area rules can be used to give advice to adjustors and to capture additional data to explain actions taken in response. When volumes are particularly high, as after a hurricane for example, the company could either soften the rules to refer fewer and/or use the models to prioritize the most suspicious claims for investigation.