Using technology to detect insurance fraud

Fraud, both detected and undetected, is a key area of ​​concern for anyone navigating the digital lifestyle.

According to an article published recently that talks about the global insurance industry, 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 shows no signs of slowing down. It is sometimes misinterpreted as a victimless crime. Consumers, on the other hand, suffer higher premiums and slower claims processing as a result of these crimes, in addition to the significant monetary and reputational losses incurred by insurance companies.

The ongoing Covid-19 pandemic is expected to increase cases of insurance fraud as reports already suggest an increase in fraud related to Covid-19. A study published by the State of Insurance Fraud Technology found that AI is becoming an increasingly important fraud detection tool as fraudsters use online and social media data for such fraudulent activities. The good news is that the Indian insurance industry has been able to curb fraudulent activities by digitizing fraud investigation.

In a survey, 68% of respondents said their organizations were using digital solutions for investigations, while 19% said they were in various stages of planning their digital transition.

Machine learning, predictive analytics, data mining methods are increasingly being used for fraud detection as timely detection is key given that there is a deterrent for fraudsters. Here are ways technology can help detect fraud in its early stages.


A network of databases, called Blockchain, records transactional data in real time. What this technology also does is highlight security, privacy and control concerns. This technology has also been hailed as an ideal solution to combating insurance fraud. The blockchain ledger maintains a permanent record of transactions that is automatically synchronized without the use of a centralizing third party. This is a process where each block is linked to a previous block and they all have a time/date stamp. If a hacker tries to change the information in one of the copies of the blockchain, the other versions will reject it as inconsistent. Blockchain is also used to prevent identity fraud in insurance practices.

Anomaly detection

Anomaly detection is one of the key trends in cybersecurity practices, with multiple use cases such as fraud prevention. In the case of insurance fraud, machine learning (ML) models help identify what a normal claim looks like to establish a baseline. Once that baseline is determined, they can identify anomalies and notify insurers. During claims processes, anomaly detection helps address legitimate customer claims. This creates a model of what a typical claim looks like when applied to larger data sets. It can also be used by insurers to detect suspicious behavior among users on their network.

Prognostic analysis

According to MarketWatchThe Global Predictive Analytics market size will reach $34.1 billion by 2027. Valued approximately at $6.9 billion in 2019, it is expected to grow at a healthy growth rate of more than 22.17% during the forecast period 2020- 2027

Similar to anomaly detection, predictive analytics involves training artificial intelligence or machine learning algorithms using historical data so that they can ultimately predict future incidents. Predictive analytics helps maintain a level of reactivity rather than proactivity.

Speed ​​up claims processing with chatbots

Reporting damage or theft to any insurance company usually initiates claim processing. Traditionally, this was done through brokers. However, with technological advancements, policyholders can now use chatbots on the insurance company’s website/mobile app to file a First Notice of Loss (FNOL). Chatbots will direct them to take photos and videos of the damage, potentially reducing the time it takes for fraudsters to alter data. Powered by natural language processing (NLP), these customer assistants speed up claim processing without the need for human intervention.

The author is Vice President – Insurance Practice, Fulcrum Digital Inc.

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