Filed Under:Claims, Auto

A Predictive Analytics Arsenal

Data Best Practices to Build Better Claims Handling

A typical auto claim process starts with a phone call from a policyholder who has just been in a car accident. The carrier representative collects details about the accident, and the claims processing system passes information to a claims adjuster’s queue. The claims adjuster then starts the investigation and may order incremental data that he or she thinks is most appropriate for each case. However, this process is labor-intensive. It may take 45 days or more to close the claim.

Now consider this scenario: an agent receives a phone call from a person who has just been in a car accident. The representative immediately sees all of the relevant data about all involved parties as it fills the screen through a data prefill product for quick validation and customer confirmation. The agent instantly confirms the person’s name, address, and vehicle identification number (VIN), as he or she collects details about the accident. Once the accident details are captured, the data is evaluated against an external database that indicates the claimant actually has coverage with multiple carriers. The claim is automatically directed to the carrier’s subrogation unit for further investigation.

Advanced Analytics Techniques
Advanced analytics techniques can help a claims organization to develop more sophisticated, efficient ways to manage the claims process. Here are some commonly used advanced analytics techniques:

  • Predictive models use algorithms to identify patterns in data. Claims are scored and then routed to the appropriate claims area, such as SIU or subrogation units.
  • Business rules alert carriers when specific situations arise. These business rules can be customized according to a carrier’s particular needs.
  • Identity matching monitors all claims for entities—people or VINs—that are on a watch list. Carriers are informed when the entity appears on a claim.
  • Data search enables carriers to search structured and unstructured data for words, phrases and names. This tool is especially valuable, as unstructured data is notoriously difficult to parse.
  • Relationship analytics find links between claims or claimants. This can be an immensely powerful tool for fraud investigations—for example, to detect links between claimants and known suspicious entities. When combined with external data, this can be particularly powerful by identifying relevant relationships that aren’t visible to a carrier using their data alone.

These tools can be even more powerful when used in combination with each other to take advantages of the strengths each tool independently brings. Identity matching and predictive model scores, for example, can be used to enhance business rules to reduce false positives and improve outcomes.

With a validated claim, carriers can apply predictive analytics to fast-track low- or no-touch claims to payment, minimizing the time and cost of handling. They can also query external data sources for information about a claimant’s history of prior claims. Excessive history may merit further attention from SIU. Additionally, carriers can find out if a claimant has coverage from other carriers, unlocking possible subrogation opportunities.

In the area of fraud detection, external data and multiple analytics tools are a tremendous combination. “Fraud detection needs to get pushed into the earlier stages of the claims process,” says Smallwood. “You still need SIU to look at aggregate data for trends, behavior, and fraud rings; however, insurers need to start to apply analytics on the front-end for real-time fraud alerts.”

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