Future Insurance Fraud Detection (I): The Essential Role of Big Data Analytics
The global insurance industry is the world’s largest business sector with an annual turnover of US$ 4.6 trillion and an asset portfolio of US$ 25 trillion based on Berkeley Lab data. However, insurance fraud is also a world problem that costs insurance companies up to millions of dollars.
Along with the advancement of digital technology, the more astute the fraudsters become in finding ways to do fraudulent acts that traditional methods can no longer prevent them. Therefore, insurance companies need the latest technology to stay one step ahead of the fraudsters, which is utilizing big data and analytics.
The role of Big Data analytics in fraud detection
We generate large amount of data every day from the use of social media, browsers, credit cards, CRM, cameras, and others. The increase in adoption of digital devices encourages the constant production of such data. These data can be leveraged to detect potential fraud.
Because of the large volume and speed of data generated from various sources, it takes some ways or methods to manage them to produce interpretation needed for the detection. This is where analytics takes over. The analytics integrates Big Data from multiple sources, including unstructured data, for example data from social media. Special investigators will agree that unstructured data is essential for analyzing fraud.
Benefits of using Big Data analytics
With Big Data analytics, the company can detect potential fraud at four stages of the insurance cycle, namely sales, claim handling, investigation, or post claim. This allows the company to have a comprehensive insight into each policyholder.
Although analyzing big data is very challenging, it allows the insurance company to understand the correlation much earlier in the policy life cycle and develop a deeper understanding related to the risk of fraud.
Based on Wsdecisionpoint data, property insurance companies that utilize Big Data and analytics reported 40% improvement in referral time, 50% more on referrals, 2.5 times the average reduction of investigation time, and the investigation cost was 1.4 times lower than the average cost of investigation of companies that do not deploy big data analytics.