Investment in Hardware/GPU: No Longer a Barrier for AI Check Fraud Detection
- AI-powered check fraud detection outperforms manual review and static rules-based systems
- Mid-range GPUs deliver strong fraud performance without unnecessary hardware expense
- Optimized AI and GPUs enable real-time, high-volume check fraud decisions
With the surge of check fraud over the past decade, we have seen more and more financial institutions transition from traditional rules-based to AI-powered check fraud detection. AI‑powered check fraud detection solutions such as OrboGraph's Anywhere On-Us Fraud and Anywhere Deposit Fraud leverage the technology to not to increase detection of counterfeits, forgeries, and alterations, but also reduce the latency -- making fraud operations more efficient.
But, as many of our readers know, a major component to run these technologies effectively is the hardware, AKA Graphic Processing Units (GPUs). Previously, we posted the reasons why GPUs are superior to CPUs for AI, noting that GPUs can handle thousands of computations and they come with dedicated VRAM (video RAM) memory that can be directed to solely handle the computations, while CPUs partition its memory to handle other tasks at the same time.
So, what does that mean for check fraud detection?
AI is Essential for Modern Check Fraud Detection
Modern check fraud is rarely obvious. AI models—particularly deep learning—can analyze check images, signatures, and transaction patterns to detect complex fraud indicators that manual review and static rules often miss. Traditional image analysis for check fraud detection typically evaluates what appears on the check, but not whether the image itself has been altered, manipulated, or synthetically created.
On the other hand, image forensics dives deeper, identifying if there are inconsistencies on the check image from previously cleared items, analyzing the handwriting/fonts on the check, the relative location of the fields of the check, and of course the consistency of the signature.
The result is higher detection accuracy and fewer false positives, even as fraud techniques continue to evolve. However, this does take immense computational power of GPUs.
GPUs are NOT a Major Barrier
Currently, there are many industry that believe that GPUs are a barrier -- with many believing that GPUs are too expensive. This is a misnomer, as there are many GPU options that can handle the workloads of a bank -- in this case, the volume of checks -- without sacrificing speed or results.
OrboGraph’s latest OrboAnywhere Turbo 6.0 release demonstrates that AI workloads can be optimized across a range of GPU classes as we have certified all the NVIDIA L‑Series GPUs. For most financial institutions, there is not a need to investment in the highest-powered, and subsequently most costly options, as L4's and L40's are able to handle the volume of most financial institutions.
This strategy balances performance, scalability, and cost.
AI is no longer optional for check fraud detection—but over‑engineering infrastructure is. With optimized models and properly sized GPUs, financial institutions can deploy scalable, high‑accuracy fraud detection without investing in flagship hardware.