$326B at Risk: Why Financial Institutions Must Embrace AI-Powered Fraud Detection
- Digital payments are on the rise
- Criminal scammers embrace technology
- More and more financial institutions are turning to AI-powered fraud detection
For financial institutions, the battle between utilizing the current legacy fraud systems vs. investing in modern fraud systems -- typically powered by AI -- continues to put pressure on both fraud departments and leadership. As noted in a recent post from American Banker, citing Juniper Research, fraud is forecasted to exceed $326 billion for the period between 2023 and 2028, showing that fraud will not be slowing down anytime soon.
American Banker provides a stark reality for those financial institutions that continue to rely on legacy fraud solutions:
Many companies in the financial services sector rely on outdated, rules-based systems for fraud detection. And while these systems can initially work well, they depend on static criteria – such as transaction limits or known fraudulent IP addresses - monitored by human security experts. Over time, these conditional statements become tangled and can't easily handle the sheer volume and complexity of online transactions.
The primary problem is sheer volume -- a major factor when it comes to all fraud, including checks.
AI-Powered Fraud Systems: Speed and Efficiency
It's no secret that AI-power fraud systems leveraging GPUs are superior in in terms of not only results, but speed and efficiency.
The article explains:
AI must not only detect suspicious activity but also process vast amounts of data efficiently. This capability is built on three core pillars - accelerated data processing, enhanced model training, and real-time model inference - each playing a crucial role in strengthening fraud detection at scale.
Additionally, these modern systems can analyze years of historical transaction data -- in a fraction of the time it takes a legacy system. For banks and payments companies that handle large volumes of data, this AI acceleration extracts actionable insights from petabytes of data, ensuring fraud models remain dynamic and responsive to new threats.
Understanding the "Three Core Pillars"
To effectively prevent fraud, American Banker explains, AI must do more than detect suspicious activity. It also needs to process vast amounts of data efficiently, a capability built on three core pillars.
AI-Powered Check Fraud Detection Systems
The article concludes that AI-powered fraud prevention systems help banks and payment companies detect, analyze, and respond to fraud in real-time. These systems continuously learn from transaction data, identify subtle anomalies, and adapt to new threats faster than traditional methods. Beyond detection, AI proactively prevents fraud by predicting emerging risks, strengthening KYC processes, and stress-testing systems to counter evolving scams.
This is particularly true when it comes to check fraud detection. AI is being leveraged in many solutions for detecting fraudulent checks, including transactional analytics, behavioral analytics, and analyzing the images of checks, known as image forensics.
These solutions utilize GPUs to train the models via vast historical data from financial institutions, along with analysis of new check transactions to identify counterfeits, forgeries, and alterations.
Financial institutions currently utilizing legacy check fraud solutions are at the biggest risk for huge losses. However, it's not too late for them, as a large number of service bureaus like Jack Henry & Associates and FIS have integrated many of these AI solutions, enabling financial institutions of all sizes to leverage these technologies without the burden on internal resources.
The technology is available; now it's on FI's to proactively deploy these solutions -- or reactively deploy after suffering massive losses.