Despite years of ongoing efforts to identify and curb insurance fraud, it remains a significant problem. Conservative estimates from the Insurance Information Institute (I.I.I.) place the figure for annual P&C payouts on fraudulent or padded claims at more than $30 billion. A further disturbing statistic suggests that 10 percent of losses and loss adjustment expenses (LAE) are associated with fraud and abuse. Thus, a carrier with $100 million in direct written premium (DWP) and running at a 70-percent combined ratio is likely leaking more than $7 million annually because of fraudulent claim activities.
As the statistics indicate, fraud continues to be a profitable enterprise, with fraudsters often operating across vertical lines like P&C insurance, healthcare, and mortgages. Economic factors play an important role as well, with the down economy driving new entrants to practice fraud—both opportunistic (such as padding, waste, and abuse) as well as organized fraud schemes. Perhaps most noteworthy are the experienced fraudsters who continually revamp and innovate, developing entirely new schemes or improving old ones to avoid detection.
The third component of effective fraud-fighting is the development and use of solutions that can identify suspicious claims systematically. In the remainder of this article, let’s focus on current and emerging analytical innovations that allow us to build more effective tools for systematic and semi-automated detection of suspicious claims.
Text mining is a recent application in extracting insights from claim notes. While claim notes pose special challenges such as typos, nonstandard abbreviations, grammatical issues, sentence fragments, and changing concepts over time, even simple and crude concept extraction can yield actionable insights. Text profiles can be created to look for adjuster descriptions of interesting concepts such as a low-impact accident¸ an accident near a highway exit, and when the claimant waives EMR/ambulance. An adjuster can use a structured field to denote the presence or absence of this concept in the notes section of each claim. Such structured fields can then be fruitfully leveraged by predictive modeling techniques.
Emerging technical capabilities, including speech recognition, image and video analysis, telematics, and novel data sources, such as license plate readers, Facebook, and Twitter, will play a key role in creating structured data attributes that can significantly enhance fraud solutions in the future.
Facing the Challenge