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According to Datos Fraud Experts, who just published their top 10 risks for 2024, fraudsters’ use of artificial intelligence (AI)-generated attacks tops the list as the number one risk. Datos’ research shows that 81% of fraud executives are concerned that fraudsters using generative AI will exceed current fraud defenses. And TransUnion confirms this, sharing 60% of auto finance fraud losses are due to synthetic identity fraud. 2024 is the year for organizations to leverage AI tools and shore up fraud defenses.

Additionally, according to a recent report from Deloitte, synthetic identity fraud is the fastest-growing financial crime in the U.S., with no signs of abating. New technologies like Generative AI have made it cheaper for criminals to buy personal data on the dark web and easier for them to create fake identities with fabricated data. In addition to being the fastest-growing, synthetic identity fraud is leading in volume – recently surpassing credit card fraud.

Other rising forms of fraud include account takeovers, in which a real customer’s identity is stolen and exploited. The most significant here is authorized push payment fraud, in which consumers are persuaded to part with their personal and financial information, enabling fraudsters to use customer credentials to access and steal funds. Merchants have also seen a rise in friendly fraud, in which a real customer is manipulating payments functions to cheat merchants out of goods or services. ACI Worldwide currently predicts friendly fraud to comprise 20% of all fraudulent transactions.

Existing infrastructure is failing today’s merchants

Failures in legacy infrastructure exacerbate the rise of fraud. Many merchants have seen a proliferation of false positives—where a genuine transaction is flagged as fraudulent—driven by static payments engines where existing models’ effectiveness has degraded. The solution is to update AI models to compensate, which costs merchants both time and money and causes either additional friction for customers or lost revenue for merchants. Many merchants operate on very thin margins, sometimes as low as 1%. This leaves little margin for error while balancing the cost of fraud against the cost of constant updates.

As if that weren’t enough, the number of digital payment options and avenues continues to grow. Every new avenue introduced to a merchant’s payments ecosystem—be it card, e-wallet, direct-to-account, cash-on-delivery, buy now pay later or others—increases the complexity of managing security. But denying access to the variety of payments options customers want increases friction and directly reduces revenue.

These combined forces have created a landscape where criminals have more sophisticated means and opportunities to perpetuate fraud, while merchants’ anti-fraud systems are struggling to keep up.

There are better ways to use data to fight crime

Merchants’ solution to increasing security while reducing costs lies in data. The more established, larger and sophisticated a merchant’s payments company is, the more data it can leverage through its payments orchestration platform in the fight against fraud. It’s a question of combining data depth with breadth and applying sophisticated AI and machine learning (ML) to spot patterns and adapt/evolve.

Merchants who work with these kinds of payments companies have an advantage. The more points along the customer journey a payments company touches—and the more strategic partnerships it has with service providers, acquirers, and banks—the larger and more comprehensive its data pool. A comprehensive data pool enables payments providers to identify and understand a consumer’s digital identity and behavioral patterns at an individual level and spot those that are anomalous. Leveraged across billions of people and trillions of transactions, this creates a powerful bank of global information on financial fraud, enabling organizations—whether merchants or banks—to make accurate decisions that focus on identifying good transactions with little to no friction while removing bias.

But having plenty of high-quality data is not enough, and traditionally has only enabled companies to look backwards – preventing future instances of known crime. This is like trying to drive forward while only being able to see through the rear-view mirror. Sophisticated AI/ML changes that equation. These systems can not only identify potentially fraudulent patterns but also predict behaviors using current and historical data at their disposal to create features and signals that anticipate and prevent new avenues for fraud while continuing to identify non-anomalous behaviors with precision. AI/ML systems are not hamstrung by legacy code because they are continuously learning, incrementally, in real time. Using this kind of incremental learning technology to run different models in parallel across trends represents a significant leap forward in the fight against fraud. For the first time, merchants can look ahead, protecting their revenue streams from new forms of crime.

Payments orchestration platforms that connect merchants to this information and technology ecosystem as seamlessly as possible are essential to preventing fraud and optimizing conversion. For high-volume merchants in particular, these gains can lead to dramatic impacts on their bottom lines.

director – Merchant Fraud Product

Amanda brings more than 15 years’ experience working in fintech to her current role as product director for ACI’s Merchant Fraud solution. Since joining ACI in 2007 she has held roles across sales, strategic relationship management and product management, with a specific focus on eCommerce fraud prevention. Amanda’s specific expertise is in leveraging data to enable risk-based screening for authentication, machine learning, artificial intelligence and behavioral analytics. Amanda applies these emerging technologies to payment fraud detection and prevention strategies. She also has a particular interest in using data intelligence for aiding conversion and removing friction from payment flows, helping to create value for ACI’s customers and key stakeholders.