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Insurance Article

Insurance Frauds: How Data Analytics and Rigorous Checks Are Trying To Eliminate Bogus Claims

April 20 2020

Insurance providers are increasingly turning to data analytics to keep frauds in check and stem identity theft

Insurance Claims

In the past, detection of fraud used to be relegated to claims agents who would rely on the available, mostly circumstantial data and intuition to validate or repudiate claims. The traditional methods proved to be very costly because of the dependence on human resources. Additionally, if the claim agents missed even a small percentage of high-value fraud cases, the financial repercussions on the insurance companies would be huge.

Today, insurance providers are increasingly turning to data analytics to quantify fraud detection and weed out bogus claims more efficiently than ever before. With the help of accurate underwriting, the potential of fraud is being detected faster now.

However, for data analytics to successfully identify and eliminate frauds, it is imperative not to repeat the mistakes of the 'Identify, Analyse, and Recommend' statistical model that insurance companies used after the fall of claim agents.

The statistical model struggled with using only a sample set to analyse data, leading to hundreds of cases going undetected. Secondly, this method had tremendous reliability of previous fraud cases because of which new types of frauds went undetected. Thirdly, this method worked in silos, incapable of integrating information from different channels and functions.

Data analytics has the potential to address all these challenges and play an essential role in fraud detection.

Fraud detection is a cumbersome process owing to data overload

While a high repudiation rate (claims that have been written back) does make sense for the insurer, it also erodes customer trust considerably.

The insurance industry is looking to strike a balance between identifying (and averting) frauds, and paying out genuine claims while weeding out the spurious ones.

As insurance providers collectively move towards digital underwriting models, it is imperative that fraud detection adapts to a more data-focused approach. Insurers will have to organize the required data pool without tampering with the customer experience.

Role of data analytics

Dealing with unstructured data gives rise to complexity. As data from one source links with the other, it becomes challenging to make sense of the maze and locate the emerging patterns. It is here that data analytics assumes significance, helping insurance providers identify discrepancies and fraudulent claims across TPAs and health networks.

If data analytics were to achieve its full potential, insurance providers would have to ready themselves for a data-oriented approach that is necessary to get a broader view of the insured across underwriting, proactive management, and claims.

Data analytics will help insurance companies drive initiatives, highlighting claims that can be paid out promptly, thus leading to increased efficiency. Additionally, it will also enable companies to focus on complex claims (that require skilful handling) instead of repudiating or rejecting them.

Data analytics will also allow companies to detect various types of frauds at different levels in the insurance lifecycle -- application, premiums, claims, surrender, employee-related, or third party frauds. This model also allows access to public records like criminal records, bankruptcy declarations, judgments, address change, and medical bill review data to identify fraudulent activity.

Data analytics for improved top-line growth

Although the leading insurers can pool structured and unstructured data from multiple sources, the common roadblock most companies are facing is linking this data across platforms and sources in real-time.

When applied correctly, data analytics will reduce the quantum of data while bettering its quality significantly. It will integrate valuable insights with the core insurance industry, thereby enabling insurers to check for fraud before underwriting or approval of claims. Besides providing a competitive edge to insurers for effective risk management, quality data also helps to enhance companies' top-line subsequently.

Continuous customer evaluation is vital for fraud detection

Consistently assessing existing customers is the key to detecting frauds early. For instance, a major red flag for potential fraud is the address or beneficiary change for new customers. Insurance providers should verify any change in address, considering most customers would be oblivious to the fact that their identities have been stolen.

Insurance companies are also considering integrating social CRM with their regular CRM activities. By leveraging relationships and people analytics, insurers can understand the connection between the owner, insured, and the beneficiary, thereby identifying and acting on suspicious behavioural patterns.

In conclusion

Insurance-related data is ever-changing. That is precisely why data analytics cannot solely depend on behavioural patterns of the past. Therefore, the information must be updated regularly. Predictive analytics can be crucial here for identifying any deliberate misselling of products or recurrent misrepresentation for that matter.

Successfully preventing fraud requires a holistic approach that recognises a fraud before policy issuance, and making use of red flags, identity checks done in real-time, and both relationship and predictive analytics. Insurance companies that can leverage these will better perceive and mitigate risks across changing customer lifecycles.

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