How Big Data Analytics Can Prevent Fraud
One benefit of your big data analytics can be fraud prevention. By many estimates, at least 10 percent of insurance company payments are for fraudulent claims, and the global sum of these fraudulent payments amounts to billions or possibly trillions of dollars. While insurance fraud is not a new problem, the severity of the problem is increasing and perpetrators of insurance fraud are becoming increasingly sophisticated.
What is the role for big data analytics in helping insurance companies find ways to detect fraud? Insurance companies want to stop fraud early. By developing predictive models based on both historical and real-time data on wages, medical claims, attorney costs, demographics, weather data, call center notes, and voice recordings, companies are in a better position to identify suspected fraudulent claims in the early stages.
For example, a personal injury claim could potentially include fake medical claims or a staged accident. Companies have seen an increase in sophisticated crime rings to perpetrate auto insurance or medical fraud. These rings may have similar methods of operation that are enacted in different regions of the country or using different aliases for the claimants.
Big data analysis can quickly look for patterns in historical claims and identify similarities or bring up questions in a new claim before the process gets too far along.
Risk and fraud experts at insurance companies, along with actuarial and underwriting executives and insurance business managers, all see big data analytics as having the potential to deliver a huge benefit by helping to anticipate and decrease attempted fraud. The goal is to identify fraudulent claims at the first notice of loss — at the first point where you need an underwriter or actuary.
Consider the following example. An insurance company wants to improve its ability to make real-time decisions when deciding how to process a new claim. The company’s cost outlay including litigation payments related to fraudulent claims has been rising steadily. The company has extensive policies to help underwriters evaluate the legitimacy of claims, but the underwriters often did not have the data at the right time to make an informed decision.
The company implemented a big data analytics platform to provide the integration and analysis of data from multiple sources. The platform incorporates extensive use of social media data and streaming data to help provide a real-time view. Call center agents are able to have a much deeper insight into possible patterns of behavior and relationships between other claimants and service providers when a call first comes in.
For example, an agent may receive an alert about a new claim that indicates the claimant was a previous witness on a similar claim six months ago. After uncovering other unusual patterns of behavior and presenting this information to the claimant, the claim process may be halted before it really gets going.
In other situations, social media data may indicate that conditions described in a claim did not take place on the day in question. For example, a claimant indicated that his car was totaled in a flood, but documentation from social media showed that the car had actually been in another city on the day the flood occurred.
Insurance fraud is such a huge cost for companies that executives are moving quickly to incorporate big data analytics and other advanced technology to address the problem of insurance fraud. Insurance companies not only feel the impact of these high costs, but the costs also have a negative impact on customers who are charged higher rates to account for the losses.
By using big data analytics to look for patterns of fraudulent behavior in enormous amounts of unstructured and structured claims-related data, companies are detecting fraud in real time. The return on investment for these companies can be huge. They are able to analyze complex information and accident scenarios in minutes as compared to days or months before implementing a big data platform.