How Artificial Intelligence Helps BNPL Providers Stay Secure – Digital Transactions

Lax credit checks, minimal personal data requested and fast loan approval. Why not like buy now, pay later (BNPL) options if you’re a cybercriminal? From creating fake identities to exploiting flaws in vendor data management tools, malicious actors have quickly taken advantage of these fast-growing and convenient financial services.

BNPL is essentially a modern take on in-store layaway and has seen a huge surge in popularity lately, with adoption rates climbing at an impressive rate. As the name suggests, BNPL services offer shoppers a quick and flexible way to purchase items they otherwise couldn’t afford right away.

But fraudsters also love BNPL, for two main reasons. First, these services have a quick and easy digital registration process and considerably looser credit checks than you find at big banks or traditional credit card companies. Fraudsters can be approved by a BNPL lender in seconds.

Rehak: “But fraudsters also like BNPL, for two main reasons.”

Yes, most BNPL companies have a robust anti-fraud process in place, but because the industry is at a relatively early stage, many of these companies rely on a variety of data sources and services to support their internal customer identification processes.

This brings us to the second reason why the BNPL is so attractive to cybercriminals: the more advanced the rating, the more pressure third-party data management is under to obtain the unobtainable – to be flawless and leave no room for error. open spaces for misuse.

BNPL suppliers want their processes to be smooth. In particular, they need the registration process to favor simplicity in order to build their brand’s market share, onboard as many new customers as possible, and then retain them. So they need to both pay attention to new customers and also protect legitimate accounts from takeover fraud and mitigate fake purchases that drain profits. And they need to be able to make accurate decisions in near real time, with high volumes of transactions being processed. Do you see the situation?

It bears repeating: Cybercriminals are becoming more sophisticated every day. In the BNPL space, malicious actors have been known to exploit everything from misconfigurations in customer relationship management to vulnerabilities in BNPL risk scoring engines to password-protected inactive user accounts. Each information leak gives cybercriminals more data (names, addresses, phone numbers, emails) to exploit in their quest for identity theft.

One solution that is taking off with several major BNPL service providers is the addition of fraud defense measures that are powered by artificial intelligence and use advanced statistical and machine learning techniques. These layers of protection monitor the provider’s underlying systems to expose patterns of fraudulent transactions and enhance the effectiveness of risk-based decision systems. Smarter monitoring and detection engines can prevent account takeovers by identifying changes in customer behavior, block spoofing attacks using intelligent and adaptive classification models, and provide protection further by identifying and exploiting similarities between seemingly unrelated transactions.

Combining multiple algorithms to detect multiple weak patterns allows current AI-based solutions to detect advanced fraud and manipulation earlier and faster than standard risk algorithms. They do this by looking for high-dimensional inconsistencies and correlations in the data that can then be investigated further. These detection engines may also flag previously unidentified vulnerabilities and gaps in third-party systems.

By distinguishing simple coincidences from unusual clusters of related transactions, these engines work closely with pre-existing underlying systems to prioritize alerts based on the full context of the transaction. The result is a significant reduction in false positive alert volumes. This both improves the efficiency of risk and fraud analysts and enables a seamless digital experience that builds brand reputation and builds customer loyalty.

The BNPL industry is walking a tightrope to ensure business security without sacrificing brand features like quick and easy approval and no-pressure payment plans. However, by increasing their automation monitoring capabilities, these vendors can gain greater market share with confidence while significantly reducing fraud.

Martin Rehak is managing director of Resistant AI, Prague.

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