The consumer marketplace has been a hot spot for fraud lately. This is due in part to the rise in more digitally focused transactions amid the pandemic and the evolving way we communicate with our banks. We’re seeing a rise in fraud risk surrounding applications that are newer to the market, like digital wallets, buy now pay later (BNPL), peer-to-peer (P2P) payments as well as those that have been in the market for a while such as bank transfers and card not present (CNP) payment methods. Consumers are attracted to these methods because they’re convenient, meaning it’s pivotal to control the rise in fraud risk to reduce customer inconvenience and friction so these more innovative payment methods can keep entering the market.
We’re seeing these obstacles primarily in industries like retail because the e-commerce space has been a big proponent of CNP payment methods. Most recently, digital wallets and Buy Now Pay Later (BNPL) have been popular options for consumers, but because they are methods that are still in their infancy, issuers and brands alike are still navigating the ultimate fraud risk.
Another, less recognized example of fraud is brand abuse. Brand abuse attacks impersonate companies through avenues like social media. And according to Outseer’s Q2 Fraud Report, this form of fraud now represents the majority of attacks today, growing from 27% in Q4 2020 to 68% in Q1 2021. Similarly, this is due to the massive shift to digital we’ve witnessed in the past 18 months.
How does machine learning monitor, detect and stop fraud?
Fraud detection at the scale the financial institutions are seeing requires sophisticated technology like machine learning and data analytics to identify harmful transactions. Unlike original rules-based fraud detection systems, machine learning automates the identification of threats through multiple avenues and proactively scans behavioral patterns, flagging any malicious activity before it causes harm. Machine learning dives deeper than the simple composition of a transaction and examines the interactions that occur prior to an attack, including the context for and relationships behind a transaction. While the information in question depends on the suspicious behavior itself, machine learning looks at variables like the type of payment being made, the origin of the payment, the speed of the interactions and transaction, or whether the payment is aligned with previous activity from that particular user.
In real-time, machine learning helps systems learn and remember various behaviors to be prepared for fraud detection down the line.
How ISVs can provide their users with advanced fraud protection
In certain markets, such as CNP, there is a well-balanced ecosystem of vendors that operate to provide the overall solution, and in many cases, integration is the most common way of enabling machine learning into an ISV. This method ensures the tight coupling of the solutions to deliver the very best performance and scale needed.
In certain situations, where an ISV has a homegrown application, they could choose to build machine learning capabilities directly into their solutions and market the platform as a single solution. This will give a more customized control of the machine learning and in some cases, a simplified operational approach.
Other methods of fraud detection do exist, however, attacks are becoming more and more sophisticated. This means the same level of sophistication needs to be in place in order to address that fraud risk – speed and accuracy is everything in this instance.
Without machine learning and data science, fraud risk isn’t completely mitigated and there’s no technology on the defensive end of potential vulnerabilities. Users and their consumers want certainty and convenience. This can’t happen without investing in the proper tools, such as machine learning, to identify and address fraud risk long before it becomes a real problem.
Advice for ISVs and software developers
It’s particularly important for those offering fraud detection services to understand the types of attacks that are most likely to occur today. This is to ensure machine learning and data science capabilities are best suited to tackle these specific risks. Phishing, CNP payment fraud and account takeover are some of the main threats we see today that can be mitigated through the proper machine learning and data science solutions. In fact, by leveraging this technology and identifying a risk score to rank the severity of the situation, Outseer successfully prevents 95% of all fraudulent transactions.
Risk scores, the output of machine learning algorithms, are an equally important factor of the fraud detection equation because it helps organizations maintain a strong user experience as well. Well-designed and proven machine learning algorithms can learn the proper balance, which helps teams manage customer experience while fraud risks are being flagged. This isn’t always the case, however, because many organizations lack the foundational knowledge, access to intelligence networks, and other necessary tools to build a fully comprehensive anti-fraud platform from scratch.
