AI-based fraud detection will continue to play a role in payments security, especially as digital payments continue to grow. Here’s what you need to know about these types of fraud tools and how they compare to rules-based models.
Fraud is incredibly expensive, which is why preventing and accurately detecting fraud is at the top of the priority list for many payments companies. Between card fraud and identity fraud and social engineering fraud, the stakes are high. The total losses from identity fraud in 2019 alone were up 15%, amounting to roughly $16.9 billion. Bad actors are also moving away from card-based fraud and increasingly turning to new account fraud (NAF) and account takeover (ATO) attacks, partly due to the effectiveness of measures to fight card-based fraud like EMV.
Yet fraud in other areas continues to rise, including in the peer-to-peer (P2P) payments arena. Scammers have become very sophisticated at convincing consumers to send funds in exchange for products or services that don’t exist. Given the sharp uptick in ecommerce and digital payments due to the pandemic, fraud will not be abated in the near future. If anything, it will continue to get worse, requiring payments organizations to get serious about fraud prevention efforts.
Fraud is an incredibly menacing problem within payments. Not only does it cost billions of dollars annually, but it is continuously evolving, with bad actors finding novel ways to exploit payments and customers at every turn. Where high-tech hacks were once the prime culprit, social engineering scams and attacks have led to global losses of more than $26 billion.
The fraud problem erodes customer trust in payments systems and costs payments organizations money. To date, many have relied on rule-based fraud systems, but this method has its own set of drawbacks:
Rules-based systems fall short in many ways, mostly because they are reactive in nature. Unfortunately, this reactivity inhibits payments organizations from efficiently and accurately preventing fraud. Alternatively, AI-based fraud detection provides real-time insights via the ability to process large amounts of transaction data and detect anomalies and other subtle changes amidst dynamic data sets.
AI and machine learning (ML) based systems can facilitate real-time processing, automatic detection of anomalies, reduced and simplified verification, and the ability to discover hidden fraud underneath subtle changes in patterns. AI- and ML-based fraud detection is also more efficient and less expensive. In fact, those systems become increasingly effective as data sets grow; the ML model is able to leverage the increased volume of data to learn and improve, detecting similarities and differences across several different data sets. The more the model learns about the difference between fraudulent and legitimate transactions, the more efficient it becomes in sorting through data sets to sort between the two.
Artificial intelligence has enabled payments organizations to more accurately detect fraud while minimizing false positives. AI-based models learn and understand the normal payment behavior of customers and leverage behavioral risk models to detect fraudulent transactions that are outside the norm. What’s more, this occurs in real-time and can facilitate the identification of emerging payment fraud schemes with high accuracy. This offers numerous benefits over a rules-based system:
As the pandemic continues to push transactions to the digital realm, payments companies must take special care to guard against existing and emerging fraud threats and vulnerabilities. The key is to do so without impeding on the customer experience or adding unnecessary friction. AI-based fraud detection enables organizations to provide sufficient protection against fraud while also reducing false alerts that can hurt the customer experience. Leveraging AI-based fraud detection is quickly becoming table stakes in an industry that is embracing a digital-first approach.
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