Oscilar, a leading provider of cutting-edge risk technology solutions to fintechs and financial institutions, has officially announced the launch of its state-of-the-art AI-powered ACH Fraud Detection product, which is designed to identify and prevent fraudulent transactions with unprecedented speed and accuracy. According to certain reports, this the new product does by banking upon advanced machine learning algorithms, generative AI techniques, as well as real-time data analysis and explainability. To understand the significance behind such a solution, we must acknowledge how threat actors today have a whole assortment of tactics at their disposal, including first-party fraud, account takeovers, stolen account details, scams, Business Email Compromise (BEC), vendor and payroll impersonation, money mules, and check kiting, each one presenting too much of a complex mechanism to unpack easily. Making the situation even more severe would be the fact that ACH credit fraud increased by almost 6% between 2021 and 2023, with more than half of organizations failing to recover funds lost from fraud attacks. Fortunately enough, Oscilar’s ACH Fraud Detection solution arrives on the scene to solve that very security skid. At launch, the product will have its operation stretched across six focal points. For starters, it will try and mitigate first-party fraud. Here, the idea is to apply Oscilar’s advanced first-party fraud detection model for the purpose of analyzing transactional data, customer history, and account status. This will uncover all those instances in which customers intentionally initiated ACH transactions despite not having sufficient funds.
The next focal point is going to be the reduction of account takeover cases. In essence, the solution will monitor device IDs and behavioral patterns to detect fraudulent ACH transactions initiated from compromised banking app accounts, thus protecting customers’ funds from unauthorized access. Then, there is a facet dedicated towards protecting the theft of account details, something which the solution will do through sophisticated Machine Learning algorithms that, on their part, will identify unauthorized ACH transactions initiated from outside the banking app with stolen credentials. Such a facet, like you can guess, will enable financial institutions to detect and prevent fraudulent activities occurring off-app.
“Oscilar’s AI-powered ACH Fraud Detection solution has been a game-changer for our company,” said Maurice Harary, CEO of Fluz. “We were facing a significant challenge with first-party fraud, where bad actors set up accounts with neobanks intending to defraud us. They would deposit the funds and then withdraw them before the ACH pull. We were on the verge of having to shut down neobanks as a funding source. Oscilar’s advanced fraud detection model quickly identified these instances, enabling us to take swift action and prevent substantial losses.”
Joining the same will be ACH Fraud Detection’s scam-centered capabilities. These capabilities involve the analysis of transaction patterns, notes and recipient information, all done to flag suspicious ACH transactions sourced from fraudulent schemes. Hold on, we still have a few bits left to unpack, considering we still haven’t discussed the solution’s take on Business Email Compromise (BEC). Basically, the development in question will resolve that particular concern by analyzing transactional data and customer behavior patterns. You see, this way it positions itself to spot anomalous payment requests and suspicious transaction patterns, and therefore, prevent unauthorized payments from being “pushed” through a payer’s account to the fraudster’s account. Moving on, the new technology will also look to expose money mules, essentially forming a pursuit where Oscilar will leverage receiver reputation signals and account behavior monitoring to identify money mule accounts used for receiving and transferring fraudulent funds. Finally, the solution will even empower you to detect ACH check kiting schemes in which criminals move money between accounts at different banks to exploit clearing house process and create the illusion of available funds when, in reality, the money has already been withdrawn.
