Insurance Fraud Detection – Data Analytics to the Rescue

May 30, 2017

financial planning

Along with low penetration of insurance products and loss-making portfolios, it is the high incidence of insurance frauds which is giving nightmares to insurers throughout the globe. According to industry estimates, in August 2016 property/ casualty insurance fraud alone accounted to around $32 billion a year. This number is just a tiny proportion of the huge volume of insurance fraud which is taking place. It also explains why fraud management has become such a critical aspect of the insurance business. Apart from anti-fraud regulations, major players in the industry are also looking for a proactive and dynamic solution which will enable them to counter such occurrences.

To know how data analytics can help in detection of insurance frauds, get in touch with an analytics expert at Quantzig.

Insurance Frauds: Top three in the list

Insurance frauds occur in different forms, shapes, and magnitude. Right from minor exaggeration in the claims to deliberate attempts of damage and accidents –insurers have to deal with a variety of fraud cases. The top three areas with high rate of fraud cases are:

  1. Claims
  2. Premiums
  3. Employee-related frauds

The bottom-line is that the occurrence of insurance frauds shoot up the premium costs for policyholders and the overall cost for the insurer. This is precisely the reason why a robust tool is required to flag such incidences.

Are you ready to empower your business with analytics? Request a FREE proposal to learn how Quantzig’s analytics solutions can help transform your business.

Can data analytics help in fraud detection?

There is no doubt that digitization of the insurance process has simplified the entire work process. Real-time information on customer data allows insurers to cross-check information and even validate pre-payment. Experts believe that analytical technology is one of the most potent tools with which sophisticated scams can be busted. Tools like claims scoring and predictive modeling help detect fraud before the payment is even made.

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