Insurers are turning their attention towards big data and analytics solutions to help check fraud, recognize misrepresentation and prevent identity theft. With the government’s recent push to adopt digitization, the Aadhaar card plays a crucial role, linking income tax permanent account numbers (PANs), banks, credit bureaus, telecoms and utilities and providing a unified and centralized data registry that profiles an individual’s economic behaviour. The e-commerce boom provides additional data on financial behaviour. 

Claims fraud is a threat to the viability of the health insurance business. Although health insurers regularly crack down on unscrupulous healthcare providers, fraudsters continually exploit any new loopholes with forged documents purporting to be from leading hospitals. 

 Medical ID theft is one of the most common techniques adopted by fraudsters. Due to this, claim funds are paid into their bank accounts, through identity theft. The insurer’s procedures allows for the policyholder to send a scanned image of his/her cheque, with the bank account details for ID purposes, which is then manipulated by the fraudsters. 

Besides forged documents, other common sources of fraud come from healthcare providers themselves, with cases of ‘upgrading’ (billing for more expensive treatments than those provided), ‘phantom billing’ and ‘ganging’ (billing for services provided to family members or other individuals accompanying the patient, but not delivered). 

 Health insurers have to take action before an insurance claim is paid and to put an end to the ‘pay-and-chase’ approach. Using data to validate a pre-payment would be far more useful than having to ‘chase’ for a payment. This approach, however, rests on real-time access to information sources. 

India’s life insurers suffer from low persistency rates that see more than one in three policies lapse by the end of the second year. This may be attributed to mis-selling, misrepresentation of material facts, premeditated fabrication and in other cases suppression of facts. 

Life insurers have been facing fraud that is largely data driven and can be curbed with effective use of data analytics. While seeking customer information, insurers should perform checks against public record databases to ensure they have insights into the validity of personal information. This can be achieved through data mining and validation from various sources. For instance, in the US, frauds are committed through stolen social security numbers or driver’s license numbers, or those of deceased individuals. Data accessed from various sources will help identify if the person in question is using multiple identities or multiple people are using the identity presented. 

 The use of public, private and proprietary databases to obtain information not typically found in an individual’s wallet to create knowledge-based authentication questions which are designed to be answered only by the correct individual can also help reduce fraud significantly. 

 Continuous evaluation of existing customers is also critical for early fraud detection. For example, one red flag for potential fraud can involve beneficiary or address changes for new customers. Insurers should verify address changes, as many consumers do not know their identity has been stolen until after it has happened. By applying relationship analytics, insurers can obtain insights into the relationship between the insured, the owner, and the beneficiary, to help determine whether those individuals are linked to other suspicious entities or are displaying suspicious behaviour patterns. 

Like in most developed insurance markets, it is imperative that data on policies, claims and customers be made available on a shared platform, in real-time. Such a platform can allow for real-time enquiries on customers. It can also facilitate screening of the originator of every proposal. Insurers would contribute policy, claims and distributors’ information to the repository on a regular basis. Such data repositories can provide insights to help insurers detect patterns, identify nexus and track mis-selling. 

 Insurance data is dynamic and hence data analytics cannot depend only on past behaviour patterns. So data has to be updated regularly. Predictive analysis can play a significant role in identifying distributor nexus, mis-selling and repeated misrepresentations. Relationship analytics could be used to identify linked sellers and suspected churn among them. 

 These data platform-based solutions are not just about preventing reputational risk and loss of business, but with controlled and more informed risk selection, there could be a positive impact on pricing of products. The whole process of underwriting new business with greater granularity of risk and greater transparency can bring in new customers, but it could also out-price some others. There can be increased scrutiny of agents, brokers and distributors to eliminate any suspects from the system. 

 Successful fraud prevention strategies include shifting towards a proactive approach that detects fraud prior to policy issuance, and leveraging red flags or business rules, real-time identity checks, relationship analytics, and predictive models. Insurers who leverage both internal data and external data analytics will better understand fraud risks throughout their customer life cycles, and will be more prepared to detect and mitigate those risks.