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Health Insurance Claim: Machine Learning for Fraud Detection

The rising medical costs put health insurance at the central part of an individual’s financial portfolio. Along with the increasing demands, insurers struggle with verifying the multiple-sourced data before paying the claims. Technology is in the help.

Fraud activities cover a wide array of improper transactions to achieve favourable outcomes from insurance companies, ranging from incident’s stating, situations’ misrepresenting, and false extent damages. These practices have accompanied insurance since its inception, and their forms have evolved together with the development of the industry.

Fraudulent is the issue

As fraudsters have continually changed their methods, insurers have faced multiple problems potentially led by the issue. On the one hand, there is the challenge of customer dissatisfaction due to prolonged investigation and delayed payouts; likewise, the cost for investigation and the pressure from insurance regulators for late payouts are high. On the other hand, potential improper payouts may cause a strike to the companies’ profit.

USD 80 billion a year is reported as the cost for fraudulent across all lines of insurance businesses. These figures are still growing, although 62% of the industry is keeping pace with the rapid technological advancement. Insurers payout up to 10% per year of their claims costs on fraudulent, in which a significant amount is associated with underwriting fraud. Underwriting fraud occurs when individuals intentionally misrepresent/conceal information at any stage of the insurance claiming process. Together with the more and more sophisticated fraud perpetrators, the need to proactively identify fraud as part of the underwriting procedure has never been more crucial.

Prevention should be from the underwriting process

Historically, the focus for fraud investigators lay within the claiming process. However, the advancement of technologies in fraud detection, like predictive analytics, can be applied at the early stage of an insurance life cycle to eliminate the event even before the claim is documented.

Most insurers depend on employees’ expertise and essential rule-based software to protect themselves from fraud. Mainly all of these methods rely heavily on a manual intervention that is easy to be outdated as fraudulent scenarios have alternated unpredictably. Consequently, insurers turn to Machine Learning with the hope to move from ”detect and react” to “predict and prevent” status. Automated processing large volumes of data, analysing multiple fraud indicators in parallel, and detect potential fraud are what underwriters can expect from co-operating the technology into the fraud prevention protocol.

Many giants in the insurance industry enthuse about implementing a Straight-Through-Process for their underwriting procedure, in which the company has the urgent need for a fraud detection engine as the backbone of the process. The process is expected to serve the ultimate goal of

The most critical factor to an underwriter is to assess the creditability of a potential customer. Processing a humongous amount of data from different sources, underwriters often struggle with concluding the risk-level in some cases. The underperformed legacy system is inflexible and fixed by a set of rules to evaluate risks and identify fraud. This system seems to be unable to detect unusual cases as fraudulent has continuously changed the way how it appears and operates. The complicated fixed rules take months to adjust or modify, thus outstretch the investigation and claim settlement. Here is when Machine Learning comes in for good.

Machine learning tools are indeed believed to act as a firewall against frauds with faster and more accurate results than when relying on human capability alone. Facing the fact that cost and processing time could hinder the adoption of Machine learning, insurers still like to view the potential of the technology with bright eyes. With the hope to continually reinventing the claims management, the industry is looking forward to smarter fraud detection, faster settlements, and better customer service.

References

Deloitte (2020). A demanding future – The four trends that define insurance in 2020. [Link]

I. Mitic (2020). The Fraudster Next Door: 30 Insurance Fraud Statistics. [Link]

FBI (2020). Insurance Fraud. [Link]

Source: Medium

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