Insurance companies are increasingly adopting “big data” analytics to better identify potential fraud, accelerate claims capabilities, and enhance brand value, according to a recent Claims Journal article titled “Claims Fraud: A Big Opportunity for Big Data & Analytics.” This blog post highlights some emerging trends addressed in the article.
“Big data” is gaining significant media attention across many industries. Tech consultant Gartner defines big data as “high-volume, high-velocity, and high-variety information assets that demand cost-effective, innovative forms of information processing.”
Proliferating communications channels – including email, fax, SMS, scanned images, and social media – make it almost impossible for insurers to proactively flag fraud using conventional data management techniques. As one example, First Notice of Loss (FNOL) is no longer limited to a simple telephone call.
Claims filings at large national insurance companies, particularly following a disaster like Hurricane Sandy, can easily qualify as big data opportunities to streamline claims processing and highlight potential fraud.
Patterns and Priorities Emerge from Big Data Analytics
Big data techniques aim to manage massive amounts of information and make it work more intelligently for insurers. According to the Claims Journal article, for example, one insurance company using big data was able to successfully handle the thousands of incoming claims it received daily in the aftermath of a major hurricane.
The insurer set up a new big data-powered infrastructure to acknowledge receipt of loss notifications, while also posting all incoming communications inside a central repository. An automated triaging solution was implemented to comb through the large volume of claims. Software solutions helped the insurer to categorize claims based on objective evidence, damage estimates, and claimant stress levels.
In the end, the insurer was able to prioritize claims, assign them to the appropriate handlers, boost the company’s image, and increase sales.
Promise of Big Data: Better, Faster Claims Authentication
Overall, potential benefits from big data can include the points outlined below.
- Big Data Can Structure Disparate Sources of Data
Inconsistent data sources pose problems for conventional data management techniques typically used during the lifecycle of a policy. Big data applications can classify and integrate “transaction,” “interaction,” and “observational” data to make it more useful.
- Big Data Detects New Dependencies and Claims Validity
Insurers receiving live data feeds (i.e., blogs, tweets, and social media posts) throughout the claims management lifecycle can better evaluate claim authenticity in near real-time. Previously static fraud scores now become more dynamic. Individual considerations within claims fulfillment (e.g. subrogation, salvage, and repair coordination) benefit from more accurate forecasts when conducting trend analyses.
- Integrated Big Data and Predictive Analytics Enhance Fraud Detection
Perhaps one of the most important advantages to using big data, according to the article’s authors, is that its analytics provide a greater capacity to identify insurance claims fraud.
New Tools in the Fight against Insurance Fraud
Organized insurance schemers pocket at least $80 billion per year in the U.S. alone, according to estimates made by the Coalition Against Insurance Fraud (CAIF). Even though fraud loss is involved in only about 10 percent of claims payouts each year, the National Insurance Crime Bureau reports that potential fraudsters view insurance scams as low-risk, high-reward ventures.
While traditional anti-fraud mechanisms like SIU units and employee training have worked up to a point, criminal rackets are moving faster than traditional monitoring techniques.
Click on the link to read “Claims Fraud: A Big Opportunity for Big Data & Analytics.”