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Keerthana Suresh

Why you should choose business analytics to reduce claims leakage?
Keerthana Suresh Software Engineer, Apps & SI Del FS-AMERICAS | April 5, 2019

Digital and Analytics – the current trend which is feeding the hunger of insurance companies to boost productivity and profitability. Alleviating claims leakage is one of the key factors that helps insurers grow in a profitable path. Leveraging the power of business analytics can largely resolve claims leakage. Read on to how.The Industry benchmark for claims leakage is about 3% but unfortunately, there is a noticed leakage of up to 25% due to poor claims management and handling. Read how to Reduce Claims Leakage @hclfs #claimsleakage @dataanalytics #insurance #insuretechClaims leakage usually occurs when a claim is settled for more than what it deserves and this becomes evident during audits subsequent to the conclusion of settlement. Auditing sessions require a skilled person to identify the root causes of claims leakage. Being at the crucial stage to mandatorily find the root cause and improve productivity, separate businesses are evolving with a group of skilled people to offer solutions for claims leakage.

Thus, it has bloomed into a vital issue for insurers. The factors that are responsible for claims leakage is illustrated in the following Fishbone diagram:

fishbone diagram

“The industry benchmark for claims leakage is about 3% but unfortunately there is a noticed leakage of up to 25% due to poor claims management and handling,” reports say.

It is not always possible to determine the primary cause via auditing, as certain causes like human errors are inevitable. Moreover, insurers cannot get effective insights through the auditing process. Introducing business analytics in such a scenario will help them find the ideal solution for this.

Applying analytics at an earlier stage will help prevent claims leakage, which is usually observed after claim settlement. As soon as the First Notice of Loss (FONL) is registered, analytics must be applied in the following order:


Take a scenario where all the above mentioned steps are followed to understand how business analytics work. Consider that a personal auto claim has come to an insurer. After the FNOL, analytics must be applied to the summarized data to find the possibilities that may lead to claim leakage. This will include factors like failure to identify coverage, lack of sufficient claim-related documentation, failure to recover subrogation, inefficient recovery through salvage, more liable to fraud, and so on.

Investigating these factors with effective claims management approaches is likely to help insurers attain the industry benchmark for claim leakage up to 3%. Analyzing the past data regarding claims is necessary as they can be given into analytics to ensure that those will not recur. With analytics in place, all the data available related to claim can be efficiently utilized to make the best out of it.

What analytics can do?

It can detect the fraudulent activities by the customer such as submitting the same proof twice, claiming in regular intervals to get back the premium paid so far, billing for procedures not actually performed, pretending to have lost expensive things, possessing double insurance, and so on.

Additionally, analytics reduces the work burden of insurers, enabling them to focus more on customer satisfaction when it comes to claim settlement. The time required to settle a claim will also be reduced, to the satisfaction of both the insurer and the insured. This, in turn, will decrease customer churn rate, lowering the company’s cost of risk, and enhancing proactive management and claim handling.


Leveraging business analytics significantly brings down the claims leakage percentage for an insurer. On the contrary, analytics should be fed right for best results. Effective implementation eliminates the root causes of claims leakage. Highlight of using analytics is that they recommend all the best possible results with evidences that can be in the form of statistics, records, proof documents, or finding fraudulent activities violating rules. This acts as a positive catalyst that provides the insurer with a more diversified and efficient approach to identify problematic claims. More focus on what is happening and predicting what is likely to happen will boost profitability in the digital era.

“The future with analytics is not so far.”
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