Online lenders employ AI-driven behavioral analysis in fight against fraud

An online buyer picks the most expensive product in the catalog without doing a price comparison, carries out the transaction very early in the morning, and hesitates when typing in personal details.

This type of behavior would raise a red flag among those tracking online fraud, and while this work has been done manually by specialist staff, financial institutions are increasingly turning to AI to help.

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“Behavioral data analysis by artificial intelligence tools is more efficient to detect fraud than manually based approaches,” said Shi Hongzhe, technology head of the US-listed consumer finance platform Lexin, which launched an AI-driven risk management platform aiming at detecting and preventing loan fraud.

“Many fraud cases cannot be identified by man-made rules,” he said.

Loans used to be approved largely based on the amount requested and the standing of the borrower, but the increasing rate of online fraud has forced the finance industry to look beyond its traditional methods of determining the reliability of a borrower. A survey conducted by PwC found that in 2018, 49% of respondents said their companies were victims of fraud, up from 36% in 2016.

The application of AI, specifically predictive machine learning algorithms, can help detect and stop fraudsters by analyzing their mobile online interactions, including the speed in which they type in personal data and the time of day they visit websites.

Teradata, a San Diego-based data analytics company, offers AI-driven fraud detection solutions to banks. One of its clients, Denmark’s Danske Bank, employed AI software to cut the number of false positives generated by human-written rules engines by 60%, and increase detection of real fraud by 50%.

Lexin’s self-developed Hawkeye platform, which every day can detect more than 500 potential fraudsters involving 3 million yuan (US$426,000), is an example of how the broader financial services industry is using machine learning to detect patterns that could signal criminal behavior.

Algorithms can detect an illicit loan application by scanning for anomalies in certain behaviors and analyzing digital information ranging from a device’s geolocation to biometric authentication. For example, the AI platform would send an alert if multiple loan applications pop up from the same device at an unusual time of day, such as dawn, when legitimate customers would not normally apply for loans, said Shi.

Some installment payment applications could also be flagged as potential fraud if the buyer does not do any price comparisons online, and instead purchases the most expensive one.

In addition, as most online fraud involves identity theft, it can often be detected if the applicant does not fill in personal identification data smoothly, indicating they have not memorized the stolen ID.

Lexin’s other business, an e-commerce platform that offers installment loans for product purchases, is also the target of scammers. In one new scam, a fraudster gains the confidence of target by convincing them to make small purchases from the Lexin platform on their behalf, offering to pay more money in return. Then the scammer does the same with a more expensive product, like an iPhone or iPad, and does not repay. The targeted “buyer” is left with the responsibility of paying off the rest of the loan.

If multiple similar cases like this are reported over a concentrated period of time, Lexin will push out warnings to its customers through its app.