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 Mainly all of these methods rely heavily on a manual intervention that is easy to be outdated as fraudulent scenarios have alternated unpredictably.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

  • reducing loss from underwriting fraudulent; while
  • keeping customer satisfaction with timely-manner payouts/compensation for authentic cases.

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.

  • Reaching the future of insurance with machine learning: By teaching the system to study and evaluate data based on both rules and artificial intelligent model, the entry data is recorded and analysed by the network, resulting in the notification for high-risk cases based on a pre-set regulations & mechanism from an artificial intelligence model. From that, a frequent pattern of fraudulent has been collected and used as a scheme to rate new cases. Any recent trends are amended shortly in a few days for a quicker claiming filing.
  • Reduce potential loss by increasing fraud prevention analysis's accuracy: Not only detect based on a certain amount of fraud sample sets, but the new system is also updated with in-time modifications from underwriters. Updated with any changes from the verification process can reveal any hidden/new factors that may lead to further fraud committing. Machine Learning upgrade a new system that increases the evaluation accuracy with more sophisticated rating algorithms from a trained AI model. Further investigation can be taken into consideration to prevent possible improper transactions during later on stage – claiming.
  • Boosting business productivity by shortening overall process time: Butomatically analysing the information from the dataset and automatically rate the risk level of each case based on a pre-set scheme can also be achieved. This flow covers from the initial contract signed customers' creditability records, risk analysis report, risk assessment, and evaluation. Underwriters do not need to manually go through all data to decide the case needs to be investigated or continue to proceed.
  • Enhance business performance by improving customer's satisfaction for claim settlement: A comprehensive, yet quick analysis and a more accurate evaluation leave more room for underwriters to pay attention to their customer service campaign or new customers' attraction activities. Adding to that, the time from document filing to claim settlement is shortened, which is an integral part of the customer rating criteria.

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.



  • 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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