Using technology to detect insurance fraud

Fraud, both detected and undetected, is a key concern area for everyone pivoting to digital lifestyles.

According to an article published recently that talks about the global insurance industry, insurance fraud costs US consumers at least $80 billion every year. It also estimates that workers’ compensation insurance fraud alone costs insurers and employers $30 billion a year.

Insurance fraud is a persistent problem that has not shown signs of slowing down. It is sometimes misinterpreted as a crime with no victims. Consumers, on the other hand, incur greater premiums and slower claims processing as a result of these crimes, in addition to the significant monetary and reputational losses suffered by insurance companies.

The ongoing Covid-19 pandemic is expected to increase cases of insurance fraud as reports already suggest the rise in Covid-19 related scams. A study released by the State of Insurance Fraud Technology found that AI has become an increasingly important tool for fraud detection, as conmen are leveraging data online and on social media for such fraudulent activities. The good news is that India’s insurance industry has been able to curb fraudulent activities by digitizing fraud investigation.

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

Machine Learning, predictive analytics, data mining methods are increasingly used for fraud detection, as timely detection is key, considering there is deterrent for fraudsters. Here are ways in which technology can help with the detection of fraud at the early stages.

Blockchain

A database network referred to as Blockchain, records transactional data in real-time. What this technology also does is it highlights concerns in terms of security, privacy, and control. This technology has also been hailed as an ideal solution to counter insurance fraud. A Blockchain ledger keeps a permanent record of transactions that is automatically synced without the use of a centralising third party. It’s a process where every block links to a previous block, and they all have time/date stamps. If a hacker attempts to change information on one of the blockchain copies, the other versions would reject it as contradictory. Blockchain is also leveraged for preventing identity fraud in insurance practices.

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 helps in identifying what a normal claim looks like to establish a baseline. Once that baseline is defined, they can identify abnormalities and notify insurers. During claim processes, anomaly detection helps in examining legitimate customer claims. This creates a model of how a typical claim appears, which it applies to larger data sets. It can also be used by insurers to discover questionable conduct among users on their network.

Predictive analytics

As per MarketWatch, the Global Predictive Analytics market size will reach $34.1 billion by 2027. Valued approximately at $6.9 billion in 2019, it is anticipated to grow with a healthy growth rate of more than 22.17% over the forecast period 2020-2027.

Like anomaly detection, predictive analytics involves training artificial intelligence or machine learning algorithms using historic data, in order for them to ultimately forecast future incidents. Predictive analytics helps in retaining a level of reactiveness rather than proactiveness.

Speeding up claims processing with chatbots

Reporting damage or theft to any insurance company generally initiates claim processing. Traditionally, it was done through brokers. However, with technological advancements policy holders could now leverage chatbots on insurance company’s website/mobile app to file the first notice of loss (FNOL). Chatbots would direct them to take photos and videos of the damage, which potentially lessens time for the fraudsters to change the data. These natural language processing (NLP) driven customer assistants speed up claim processing, without the requirement of human intervention.

The writer is VP - Insurance Practice, Fulcrum Digital Inc.