Let’s do something a bit different here and start with the ugly, shall we?
Every one in ten claims in the insurance industry is fraudulent. For insurance companies, fraud continues to be one of the single most cumbersome – and costly – issues facing the industry today. According to a study released in August 2016 at the Institute for Insurance Information, insurance fraud is responsible for an estimated $77 billion – $259 billion of losses per year in the health industry.
The Bad? For insurers, these billions of dollars in annual capital loss is only the tip of the iceberg. According to IBM Big Data and Analytics Training Hub, insurance fraud expenses translate into higher premiums for policyholders and puts the burden of fraud on to the backs of law-abiding customers who certainly don’t want to see a spike in their annual premiums.
So, is there a Good?
Fortunately, yes. The rapid development of technology has resulted in the emergence of the single most versatile and efficient tool for detecting and combating fraudulent activity: big data analytics. Data analytics technology for fraud prevention enables insurers to identify red flag symptoms early, significantly increase detection accuracy and reduce the time required to monitor claim activity.
According to a 2013 FICO Insurance Fraud Survey, many insurers have already begun to move away from outdated rules-based systems to more agile, analytics-based solutions. Almost 45% of survey respondents reported they use predictive analytics to detect and combat fraud, while only 29% continue to use rules-based systems.
While these numbers imply that insurers are beginning to jump on the data analytics bandwagon, experts suggest that there has never been a more powerful time for the remaining half to move to an analytics-based solution.
Why Are Analytics Important?
Because fraud can be committed at the applicant, claimant, or policyholder level, a robust fraud prevention solution must be able to detect questionable activity on multiple levels. Some of the most common types of fraud include:
- Inflating claim amounts, or “padding”.
- False representation of facts during application intake.
- Claims for untrue accidents or injuries.
- Staged accidents.
New data analysis tools can intelligently recognize and flag emerging schemes to detect atypical behavior in its earliest stages. According to a recent study, analytics tools are particularly useful for fraud review and detection in areas including underwriting and policy renewals.
Predictive data analysis combines rule monitoring, modeling, text mining, database searches and exception reporting to identify fraud sooner and more efficiently at every stage of the claim life cycle.
Analytics, Fraud Detection, and the Way Forward
While data analytics offer a tremendous asset to insurance companies looking to manage and reduce fraud, insurers should also keep in mind that regular scrutinous observation of trends on a business level is essential. Analytics provide an automated tool to track and measure data, while significantly reducing time spent and risk of human error.