How Big Data Analytics Can Prevent The Problem Of Insurance Fraud

How Big Data Analytics Can Prevent The Problem Of Insurance Fraud

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Insurance fraud is a rising concern for insurance companies. Insurance fraud spans across all types of insurance and false claims regarding car theft, fire damage, storm damage, injuries or medical procedures all carry huge risks for insurance companies. Fraudulent claims can be made by both individuals and companies, making the size of the risk expand as claims are filed. Companies that are victims of insurance fraud can potentially lose large amounts of revenue. Insurance consumers are also victims of insurance fraud because rates can be raised as a result of the lost revenue incurred due to fraud.

How Can Companies Fight Back Against Insurance Fraud?

Insurance fraud can be extremely difficult to prove. Insurance companies typically utilize private investigators to find evidence of faulty claims. In addition to private investigators, insurance companies rely on law enforcement and the court system to gather information surrounding details of suspicious claims. This approach to insurance fraud has required insurance companies to rely on outside parties to mitigate and resolve the issues they incur through faulty claims. These approaches are reactionary don’t allow insurance companies an opportunity to be proactive about preventing insurance fraud. The amount of time and effort necessary to track down insurance fraud is a main contributor to why so many cases of insurance fraud have traditionally slipped through the cracks. However, modern technology now allows insurance providers the ability to use big data to help anticipate and detect fraud before false claims are paid out.

Making Every Piece of Data Count 

Real time big data analytics gives insurers the ability to process and assess a larger volume of claims than allowed by standard models. Using big data to analyze these claims takes away the need for people to manually evaluate claims. Claims that seem suspicious can be automatically flagged and isolated for further review. Big data platforms can be tailored to flag pieces of data that indicate discrepancies or specific to certain keywords or dollar amounts.

Accountability at Every Step

Insurance fraud accounts for billions of dollars of loss each year. Big data is useful because it allows an insurance company to use the data that it collects more effectively. Insurance companies collect large volumes of data on a daily basis. The data is collected during the application process, while accounts are being serviced and when claims are being processed. This data is typically processed independently. As a result, insurers do not have a holistic picture of the data for each account holder. Having a full set of data allows insurers the ability to more accurately evaluate claims as they come in. Big data also enables an insurer to more quickly pay legitimate claims.