Tracking payment fraud, money laundering, insurance fraud and identity fraud is an expensive and time-consuming process due to the large volumes of financial fraud data which must be analyzed.

Advances in digital banking, online account opening, open banking and cryptocurrency make it difficult to track the source of funds and locate fraud. Financial organizations are increasingly using cloud-based, GPU-accelerated artificial intelligence (AI) and machine learning (ML) predictive analysis models to identify fraud transactions quickly and accurately.

Traditional fraud tracking methods are ineffective tools

Many organizations use legacy CPU-based processing infrastructure and Transactions Monitoring Systems (TMS) to identify suspicious transactions that may involve fraud or legitimate proceeds used for illegal purposes. These system are typically antiquated rules-based systems that rely on structured queries that aren’t precise and can generate high false positive alerts.

Building an effective AI financial fraud solution

AI is specifically suited to detect financial fraud because it picks up patterns that humans can’t easily interpret. Financial services organizations are increasingly using AI and ML predictive data models running on modern infrastructure to analyze financial or account data to help locate anomalies that indicate evidence of potential fraud.

Moving to a cloud-based GPU-accelerated infrastructure provides faster processing and training for ML inference models needed to analyze the massive amounts of data to locate fraudulent finance data. As described in this article, American Express uses and NVIDIA GPUs and long short-term memory networks, or LSTMs, running AI anomaly fraud detection on tens of millions of daily transactions. Using the NVIDIA solution, American Express saw a 50x improvement over CPU processing.

According to this IDC report, Worldwide CEO Survey, 2022: Industry Perspectives, August 2022, “44.6 percent of financial services respondents consider driving more revenue-generating activities the most critical technology initiative for their organizations, while 41.1 percent are focused on delivering digital services faster and accelerating the shift to the cloud.”

NVIDIA’s “State of AI in Financial Services” survey found that the use of AI for fraud detection for know your customer (KYC) and anti-money laundering (AML) compliance was one of the top AI solutions implemented between 2021 and 2022.

Technology partners provide cloud-based, GPU-accelerated AI fraud detection solutions

Microsoft and NVIDIA have a long history of working together to support financial institutions in providing technology to support AI and ML solutions used in financial fraud detection. Using Microsoft Azure cloud and the NVIDIA AI platform provides scalable, accelerated resources needed to run AI/ML algorithms, routines, and libraries.

The partnership between Microsoft and NVIDIA makes NVIDIA’s powerful GPU acceleration available to financial institutions. The Azure Machine Learning service integrates the NVIDIA open-source RAPIDS software library that allows machine learning users to accelerate their pipelines with NVIDIA GPUs. The NVIDIA TensorRT acceleration library was added to ONNX Runtime to speed deep learning inferencing. Azure supports NVIDIA’s T4 Tensor Core Graphics Processing Units (GPUs), which are optimized for the cost-effective deployment of machine learning inferencing or analytical workloads.

Microsoft cloud-based solutions for financial fraud detection

Moving to the Microsoft Azure cloud solution provides financial institutions with a complete set of computing, networking, and storage resources integrated with workload services capable of handling the requirements of AI algorithm processing. Organizations can use Azure Stream Analytics to do serverless real-time analytics of financial data from existing repositories, so that fraud prevention teams can access that data in real-time. Automating processes with technologies like Microsoft Power Platform aids in catching fraudulent activities as they occur.

Summary

Financial institutions are required to track and report potential financial fraud in areas such as money laundering or insurance fraud transactions. Using AL and ML algorithms running on GPU-accelerated cloud-based solutions can analyze patterns in financial data to accurately identify fraudulent transactions. This helps financial organizations save staff time, and can aid in reducing fines for non-compliance in identifying financial fraud transactions.