A recent Ficci report says the number of false claims in the health insurance industry is over 15% and it is losing about R1,000 crore every year because of these. Experts say Big Data can help check fraudulent claims to some extent. Health insurance frauds could relate to concealing pre-existing diseases or chronic ailments, manipulating pre-policy health check-up findings, furnishing duplicate or inflated bills, purchasing multiple policies, staging accidents or faking disability claims.
In most cases, fraudulent health insurance claims are actually made to cover up information. Such frauds could be of many types and committed by an insured or a hospital. Opportunity fraud is created by a policyholder by over-stressing a genuine claim or hiding information related to pre-existing diseases. A proper data analysis of medical records can detect whether the insured is hiding any pre-existing disease. If a consumer enters an illegal claim, data analysis from hospitals can detect fraud.
Agents and brokers, too, could be involved in fraud; they could be providing fake policy to customers and siphoning off premium, manipulating pre-policy health check-up records, guiding customers to hide pre-existing diseases or chronic ailments and fudging data in group health cover. Also, due to absence of standard medical protocol and lack of refulatory oversight, hospital-induced fraud, such as overcharging, inflated billing, unwarranted procedures, extended length of stay, fudging records and patient history and even billing for services, form a large portion of fraudulent claims.
Analysts say the industry will have to work closely with the Insurance Information Bureau to create benchmarks, which can be used by individual stakeholders to obtain better insight into their overall performance. Aggregating industry data in a single data warehouse and, then, developing benchmarks with which an individual insurer could make comparisons may help check fraud, says Saurabh Sharma, an insurance broker.
According to an Ernst & Young survey, the average ticket-size of a single fraud ranges between R25,000 and R75,000. In 2010, some of the leading public sector companies had a major standoff on treatment costs with providers due to which they de-empanelled many network hospitals from their existing list and, later, restored it by forming a preferred provider network. Fraud and dishonest claims are not only a major hazard for the insurance industry but also for the country's economy. To this effect, the Insurance Regulatory and Development Authority (Irda) has mandated sharing of claims data. The regulator has also issued guidelines on health insurance, covering several contentious issues like standardising treatment procedures and costs.
At present, fraud is not properly defined under the Indian Insurance Act and even other instruments such as the Indian Penal Code (IPC) or Indian Contract Act do not offer specific laws related to fraud in health insurance. Sections of the IPC, which deal with issues of fraudulent act, forgery and cheating are sometimes applied, but none of them are specifically targeted at insurance fraud and are inadequate for the purpose of acting as an effective deterrent, says the Ficci working paper on health insurance fraud.
Pre-authorisation can be the first-level check to curb fraud and can eliminate or reduce the likelihood of its occurrence. Pre-authorisation requests for scheduled surgeries must be submitted at least 24 hours before admission and the implementation of a standardised discharge summary and billing format can check occurance of fraud. Deployment of robust technology and a data analytics process can detect outlier behaviour and be an early warning system for detecting fraud.
Providing wrong, misleading and incomplete information on age, occupation, lifestyle
Not disclosing previous ailments or giving information on chronic ailments
Inflated billing and billing for services not rendered
Fabrication of duration of admission to
meet 24-hour hospitalisation clause
By distribution channels
Non-adherence to underwriting guidance
Fabrication of pre-acceptance health check
Kickbacks from provider network, collusion for inflated bills
Listing non-existent employee in employers list for claims