Using technology to detect insurance fraud

Fraud, both detected and undetected, is a key area of ​​concern for all adopters of digital lifestyles.

According to a recently published article discussing the global insurance industry, insurance fraud costs American consumers at least $80 billion each year. He also estimates that workers’ compensation insurance fraud alone costs insurers and employers $30 billion a year.

Insurance fraud is a persistent problem that has shown no signs of slowing down. It is sometimes misunderstood as a victimless crime. Consumers, on the other hand, incur higher premiums and slower claims processing as a result of these crimes, in addition to significant monetary and reputational losses suffered by insurance companies.

The ongoing Covid-19 pandemic is expected to increase insurance fraud cases as reports already suggest an increase in Covid-19 related scams. A study published by the State of Insurance Fraud Technology found that AI has become an increasingly important tool for fraud detection as fraudsters are leveraging data online and on social media 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 one survey, 68% of respondents said their organizations were using digital solutions for investigations, while 19% said they were in various stages of planning to go digital.

Machine learning, predictive analytics, and data mining methods are increasingly being used for fraud detection, as timely detection is key, considering there is a deterrent to fraudsters. Here are ways technology can help with early-stage fraud detection.

block chain

A database network known as Blockchain records transactional data in real time. What this technology also does is highlight concerns in terms of security, privacy, and control. This technology has also been hailed as an ideal solution for countering insurance fraud. A Blockchain ledger maintains a permanent record of transactions that is automatically synchronized without the use of a centralizing third party. It is a process where each block is linked to a previous block and all have date and time stamps. If a hacker tries to change the information in one of the copies of the blockchain, the other versions will reject it as contradictory. Blockchain is also being leveraged to prevent identity fraud in insurance practices.

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Anomaly detection

Anomaly detection is one of the key trends in Cybersecurity practices, with numerous 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 defined, they can identify anomalies and notify insurers. During claim processes, anomaly detection helps examine legitimate customer claims. This creates a model of how a typical claim appears, which applies to larger data sets. It can also be used by insurers to uncover questionable behavior among users on their network.

predictive analytics

according to market clockGlobal Predictive Analytics market size will reach $34.1Bn by 2027. Valued at approximately $6.9Bn in 2019, it is anticipated to grow with a healthy growth rate of over 22.17% during the forecast period 2020-2027.

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

Speeding up claims processing with chatbots

Reporting damage or theft to any insurance company generally initiates claims processing. Traditionally, it was done through intermediaries. However, with advancements in technology, policyholders can now take advantage of chatbots on the insurance company’s website/mobile app to file First Notice of Loss (FNOL). The chatbots would instruct them to take photos and videos of the damage, potentially reducing the time for scammers to change data. These natural language processing (NLP)-powered customer assistants speed claims processing, without the need for human intervention.

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The author is vice president of insurance practices for Fulcrum Digital Inc.

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