Key Points
  • Insurers are struggling to manage rising cases of fraud due to obsolete technology and shortage of skilled resources

  • Predictive modeling, geographic data mapping, social media analytics and text mining are effective tools to reduce false claims

  • Analytics combined with artificial intelligence helps identify fraud patterns and anomalies from large sets of data

False and improper claims accounted for USD 2.5 Billion recovered under the False Claims Act in 2018 in the U.S. Insurers are painfully aware of the impact of fraud on their profitability. But the classic roadblocks to managing them effectively continue to plague the industry. Chief among these challenges are obsolete technology, the deployment of resources who lack specialized skills and fraud detection not being a priority area of focus.

By leveraging predictive modeling, geographic data mapping, social media analytics and text mining across certain stages of a policy life cycle, businesses can effectively detect fraud and significantly reduce claims costs.

In the underwriting stage, advanced analytics helps capture the identity of the customer, linkages to fraud and other abnormal behavioral patterns. At the First Notice of Loss (FNOL) stage, analytics helps claim handlers validate a larger number of claims with greater accuracy in a shorter period of time. Real-time insights help resources change their line of questioning as warranted and refer suspicious claims to investigators.

In the investigation stage, the veracity of claims can be validated by analyzing and cross-referencing data from disparate sources, and highlighting hidden relationships. For example, social network analytics helps investigators in identifying linkages of the claimant with fraudulent activities.

When there are a large number of claims, analytics helps identify patterns of fraud that are not easily detectable at the level of individual claims. The correlation between volumes of data helps in identifying organized fraud, and updating the rules and operating models across other stages of the policy lifecycle.

The impact of analytics is amplified with the help of Artificial Intelligence (AI) which can sieve through large data sets, identify patterns and anomalies based on algorithms, and flag cases for review. AI plays a vital role in insurance fraud management as data sources vary across stages — application data, claims reports, police reports, medical reports, and external reports such as industry fraud alerts and watch list data. Not surprisingly, many leading insurance companies are leveraging the combined power of analytics and AI to combat fraud.

Technology is a double-edged sword. Not only is it enabling insurers with tools to fight fraud, but it is simultaneously equipping fraudsters with more sophisticated ways to commit fraud. While lawmakers try to keep pace with the evolving nature of fraud, analytics appears to be the insurers’ insurance against fraud.

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