Using technology to detect insurance fraud

Fraud, both recognized and undetected, is a critical concern for anyone pursuing a digital lifestyle.

According to a recently published article covering the insurance industry worldwide, insurance fraud costs US consumers at least $80 billion each year. It also estimates that workers’ compensation insurance fraud alone costs insurers and employers $30 billion annually.

Insurance fraud is an ongoing problem that has shown no signs of abating. It is sometimes misinterpreted as victimless crime. On the other hand, consumers receive higher payouts and slower settlements as a result of these cases, in addition to the significant financial and reputational losses that insurance companies face.

The ongoing Covid-19 pandemic is expected to increase insurance fraud as reports are already showing a rise in Covid-19-related theft. A study released by the State of Insurance Fraud Technology found that AI has become an important tool in fraud detection, as fraudsters are using the internet and social media for such fraud. The good news is that insurance companies in India can combat fraud by using fraud detection systems.

In a survey, 68% of respondents said their organizations are using digital solutions for research, while 19% said they are in various stages of digital transformation planning.

Machine learning, predictive analysis, mining techniques are increasingly used in fraud detection, as timely detection is essential, considering that there is a deterrent to fraudsters. Here are some ways technology can help detect fraud early.


A network of databases called Blockchain, records transactions in real time. What this technology also does is highlight concerns about security, privacy, and control. This technology has also been cited as an effective way to combat insurance fraud. The Blockchain ledger keeps a permanent record of transactions that are automatically linked without the use of a third party. It’s a process where each log is linked to the previous log, and they all have time/date stamps. If a hacker tries to change information on one of the copies of the blockchain, the other versions will reject it as conflicting. Blockchain is also used to prevent insurance fraud.

A strange realization

Anonymous detection is one of the most important aspects of Cybersecurity, with many use cases such as fraud prevention. In the case of insurance fraud, machine learning (ML) helps identify what regular claims look like by establishing a baseline. Once this is known, they can identify errors and notify insurers. During the application process, non-conformance detection helps to assess the customer’s needs. This creates a model of what the claims look like, which works on large sets of data. It can also be used by insurers to detect suspicious behavior among users on their networks.

Statistical predictions

As in MarketWatchThe size of the Global Predictive Analytics market will reach $34.1 billion by 2027. It was valued at $6.9 billion in 2019, and is expected to grow at a CAGR of more than 22.17% during the forecast period 2020-2027.

Like abstract sensing, predictive analytics involves training an artificial intelligence or machine learning machine using historical data, so that it can predict the future. Predictive analytics helps maintain a level of engagement rather than static.

Speeding up claim processing with chatbots

Reporting the damage or theft to any insurance company usually results in a renewed claim. Traditionally, it was done through brokers. However, with the advancement of technology policyholders can now use chatbots on the insurance company’s website/mobile app to file a first notice of loss (FNOL). Chatbots guide them to take pictures and videos of the damaged data, which can reduce the time for fraudsters to change the data stored. These natural customer support (NLP) processes accelerate claims processing, without the need for human intervention.

The author is VP – Insurance Practice, Fulcrum Digital Inc.