Machine Learning Times
Machine Learning Times
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CONTINUE READING: Access the complete article in the Insurance Networking News, where it was originally published.  

7 years ago
Big Data Already Paying Off in Insurance Fraud Detection


There’s a lot of talk right now about how big data is going to change insurance. Artificial intelligence and prescriptive analytics are definitely coming, and they will definitely change the insurance industry. To get a better idea of the specific ways in which big data applications are going to play out, it’s worth examining how data analytics are being used in the industry right now.

Many insurers’ initial forays into the world of big data focus on fraud detection. That makes sense — the Insurance Information Institute (III) estimates that 10 percent of property-casualty insurance industry losses each year are attributable to fraud, to the tune of $32 billion.

And most believe the problem is getting worse: 61 percent of property-casualty insurers report that the number of suspect frauds increased slightly or significantly over the past three years, according to a 2016 white paper issued by the Coalition Against Insurance Fraud. Insurance companies have always relied on technology to fight fraud. The III reports that 95 percent of insurers say they use antifraud technology, but about half say a lack of information technology resources prevents them from fully implementing it.

Enter big data. At the beginning of 2016, Towers Watson reported that 26 percent of insurance companies were using predictive analytics to address fraud potential. In the next two years, that number is expected to jump to 70 percent—more than any other big data application.

Putting Text Mining to Good Use

As data scientists team up with claims department leaders and other insurance professionals, many are looking to text mining as a crucial analytical tool for decoding enormous amounts of unstructured data.

Put simply, text mining is a way to scan large amounts of data for keywords, not unlike a web search. But claims departments are putting the technology to more sophisticated use, analyzing information for more significant data points and connections. Text mining can interpret claims adjusters’ handwritten notes and scan a claimant’s social media accounts for suspicious activity in nearly real time.

A Shift in Focus for Claims

Text mining and other tools represent a fundamental shift in how claims departments seek out fraud. ACORD has stated that, thanks to big data, fraud detection will move from being claims-centric to person-centric. In other words, efforts to spot fraud will shift from focusing on the claim itself to the individual filing the claim.

Claims models will pull information about the would-be beneficiary from across claims, policies and external data sources, including information from other insurers, medical professionals, police, auto body shops and a host of other sources.

Privacy and Accuracy Concerns

This shift toward person-centric fraud detection and an increase in shared data raises issues regarding privacy and data quality. Individuals may opt out of sharing information with their insurers through vehicle telematics or social media, lessening the impact of insurer data analytics initiatives and creating a competitive advantage for less scrupulous organizations willing to harvest data against consumers’ wishes. Compounding the issue is the fact that data collected is not always accurate or easily manipulated.

CONTINUE READING: Access the complete article in the Insurance Networking News, where it was originally published.

By: Michael Elliott, Senior Director of Knowledge Resources, The Institutes
Originally published at

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