Insurance law firm BLM launches Fraud Edge tool giving insight on fraudulent activity

BLM | July 21, 2020

Insurance law firm BLM launches Fraud Edge tool giving insight on fraudulent activity
As the battle against insurance fraud rages on, insurance law firm BLM believes it may have a vital new weapon to help win the war. The firm is introducing what it describes as a “game-changing” tool, known as Fraud Edge. It gives greater levels of insight on fraudulent activity to help the user form their claim investigation strategy.
Among its key features, is an “Innovations” offering, which offers the full spectrum of previous claims and fraud data. This helps avoid oversights – for example, if a suspected fraudster had previously been involved in a questionable motor claim, this may not have otherwise been picked up on a holiday sickness claim. In addition, it offers an analysis of the track record of the claimant firm and medical practice involved.

Spotlight

As insurers prepare themselves to leverage Big Data either by harnessing data that is available internally or through partnerships, they need to assess the preparedness to manage such data. Traditionally, insurers have been used to managing structured data that comes as part of various business processes. Harnessing new sources of data will require the ability to store and process semi-structured and unstructured data such as customer interactions, images, medical records. Multiple trade-offs including costs, speed, functionality, scalability, data diversity are involved while choosing the right data architecture in line with business objectives. Given the selection and implementation lead times, this becomes not only important but also urgent for parallel design and execution of business strategy.

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Spotlight

As insurers prepare themselves to leverage Big Data either by harnessing data that is available internally or through partnerships, they need to assess the preparedness to manage such data. Traditionally, insurers have been used to managing structured data that comes as part of various business processes. Harnessing new sources of data will require the ability to store and process semi-structured and unstructured data such as customer interactions, images, medical records. Multiple trade-offs including costs, speed, functionality, scalability, data diversity are involved while choosing the right data architecture in line with business objectives. Given the selection and implementation lead times, this becomes not only important but also urgent for parallel design and execution of business strategy.