How Analytics Is Helping Insurance Companies In Detecting Claim Frauds

How Analytics Is Helping Insurance Companies In Detecting Claim Frauds

By Souvik November 12, 2020 - 3,936 views

“Global Incidence of Insurance frauds sums up to 3.58% annually”– Global Claims Fraud Survey. In many countries, it takes up to 8% of its revenue annually. Now, that is a huge concern for the Insurance Companies. 

The sudden outbreak of COVID-19 and the resulting pandemic has acted as a catalyst for the insurance sector as the demand for health insurance has increased profoundly. In countries like India, where only 10% were previously interested in securing their health by buying health insurance, this has increased to 71%

The average claim size has increased manifold in the recent turbulent time. Surveys say insurance companies are taking a vast hit from the pandemic. Another problem that arose amidst these is the “Insurance Scam.” 

A worldwide lockdown has paused many businesses’ operations, therefore leading to loss. As profits dwindle, businesses fail to cover up the average variable cost, pushing many businesses towards bankruptcy. Thus, increasing incidents like staged thefts, torching machinery, fake injuries, medical scams, vehicle scams, etc. 

Hence, there is an urgency to exploit analytics to detect potential threats. In the upcoming section of the blog, we will discuss how insurance companies can exploit analytics to prevent scams. 

Predictive Analytics

In a legacy model, insurers depend largely on intuition to detect false claims. Intuition fails, and many times the failure rate is quite high. In the pre-covid situation, insurers visit places to cross-check and validate the documentation of the reported events. Covid has stalled the legacy procedure because social distancing is the new normal norm that has to adhere strictly to combat the virus. Hence, insurers are heavily relying on analytics to overcome these challenges. 

Moreover, we came to know about more challenges that insurers face while providing them with our digital solutions. Among them, the most common one is the detection of outlier claims. If done manually, fraud detection is an exhausting process, and insurers consistently focus on reducing the cycle time in this fast-paced digital era. Chances of fraud getting undetected increase if the manual process is followed.

Today, Data is linked to various sources, and the right integration can make detection easier. Thus, insurers are harnessing digitization by deploying predictive analytics to detect or prevent insurance fraud.

From our client testimonial, we found the benefits they reaped by deploying predictive analytics to detect a false claim. So, the claimant documented a scene where his factory caught fire. The story was so compelling that detecting the staged event manually was next to impossible. Predictive analytics then came in handy.

As claimants file up, advanced clustering techniques are used to categorize different clusters with the level of frequency for claims. The algorithm model calculates the probability of the claim being above the threshold level. If it is above, then it will directly go to the special investigation unit. 

With the help of historical data and real-time data, by using predictive analytics, early detection of fraudulent claims are possible. Datasets gathered from the claimant’s interaction on social media platforms on their lifestyle and other social factors were used as sentiment analysis by deploying advanced algorithm models to further help in detecting false claims.

For example, if a claimant’s check-in location on social media platforms is different from the reported location of the incident during that particular time, the advanced algorithm could track it and defend the claim. 

Speech Analytics

Insurance companies are also using voice analytics or emotion analytics to detects the claimant’s false claims by analyzing their voice data. Speech analytics uses crafted mathematical and statistical algorithms to detect any fraudulent activities at the FNOL (First Notice of Loss) stage.

If a person sounds different than the demanded situation while filling up the claim in the open conversation, AI attached to it will detect the anomaly and send it for further investigation. 

Speech analytics can further be used for the following benefits apart from detecting frauds: 

  • Speech analytics with trend-based studies can ease the burden on call centres for sudden calls during natural calamities. 
  • Speech analytics can monitor the timings of the calls and the factors behind fuelling cost. Thus helping in minimizing expenses. 
  • Voice analytics can intercept every conversation with the insured and flagging the one demanding deep sighted conversation with the insurers. 


Telematics can be exploited as an advanced investigating tool for detecting insurance fraud. Telematics can signal out high-risk behaviour such as driving above the safer range. If the signal is ignored, then the claiming process can be deterred. Also, data from the accelerometer can be used to identify the car’s speed during the crash. 

Deploying ML and AI in the Dashcam helps in identifying the truth of the incident. Regular real-time data collection on the driver’s driving performances have consistently monitored them, thus preventing accidents or false claims. 

Telematics also helps in profiling the driver’s behaviour in the motor insurance sector. Motor insurance rewards the driver with a lower premium if his driving skills fall under a safer slot. This kind of insurance is also known as Usage-Based Insurance.

Final Take Away

Insurance companies worldwide are likely to favor digital disruption to stay future-ready to navigate through unforeseen crises. Insurers normally follow the legacy model that depends on the manual process, but a sudden outbreak of COVID-19 doesn’t permit us to follow. Social distancing and economic downturn only exacerbate the problem. The role of technology is becoming more crucial every day. The acceleration of digitization premature due to the pandemic. Emerging Leaders in the insurance sector exploit analytics for decision making in insurance-related operations such as claim settlement, policy administration, underwriting, and fraud detection & prevention. 

Let us delve into the major take away:

  • Leveraging telematics to measure risk score using data scores from wearables can flag any risky event and proactively communicate with the clients to minimize the risk. 
  • Leveraging speech analytics to detect odd claim patterns by analyzing audio to prevent loss. 
  • Predictive Analytics helps insurers re-running the analysis based on the data trends followed in fraudulent claims to prevent further loss. 
  • Digital solutions also cut in operational costs and ease the exhausting process of fraud detection.
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