A Novel AI Workflow from NVIDIA Identifies False Credit Card Transactions.
The process, which is powered by the NVIDIA AI platform on AWS, may reduce risk and save money for financial services companies.
By 2026, global credit card transaction fraud is predicted to cause $43 billion in damages.
Using rapid data processing and sophisticated algorithms, a new fraud detection NVIDIA AI workflows on Amazon Web Services (AWS) will assist fight this growing pandemic by enhancing AI’s capacity to identify and stop credit card transaction fraud.
In contrast to conventional techniques, the process, which was introduced this week at the Money20/20 fintech conference, helps financial institutions spot minute trends and irregularities in transaction data by analyzing user behavior. This increases accuracy and lowers false positives.
Users may use the NVIDIA AI Enterprise software platform and NVIDIA GPU instances to expedite the transition of their fraud detection operations from conventional computation to accelerated compute.
Companies that use complete machine learning tools and methods may see an estimated 40% increase in the accuracy of fraud detection, which will help them find and stop criminals more quickly and lessen damage.
As a result, top financial institutions like Capital One and American Express have started using AI to develop exclusive solutions that improve client safety and reduce fraud.
With the help of NVIDIA AI, the new NVIDIA workflow speeds up data processing, model training, and inference while showcasing how these elements can be combined into a single, user-friendly software package.
The procedure, which is now geared for credit card transaction fraud, might be modified for use cases including money laundering, account takeover, and new account fraud.
Enhanced Processing for Fraud Identification
It is more crucial than ever for businesses in all sectors, including financial services, to use computational capacity that is economical and energy-efficient as AI models grow in complexity, size, and variety.
Conventional data science pipelines don’t have the compute acceleration needed to process the enormous amounts of data needed to combat fraud in the face of the industry’s continually increasing losses. Payment organizations may be able to save money and time on data processing by using NVIDIA RAPIDS Accelerator for Apache Spark.
Financial institutions are using NVIDIA’s AI and accelerated computing solutions to effectively handle massive datasets and provide real-time AI performance with intricate AI models.
The industry standard for detecting fraud has long been the use of gradient-boosted decision trees, a kind of machine learning technique that uses libraries like XGBoost.
Utilizing the NVIDIA RAPIDS suite of AI libraries, the new NVIDIA AI workflows for fraud detection improves XGBoost by adding graph neural network (GNN) embeddings as extra features to assist lower false positives.
In order to generate and train a model that can be coordinated with the NVIDIA Triton Inference Server and the NVIDIA Morpheus Runtime Core library for real-time inferencing, the GNN embeddings are fed into XGBoost.
All incoming data is safely inspected and categorized by the NVIDIA Morpheus framework, which also flags potentially suspicious behavior and tags it with patterns. The NVIDIA Triton Inference Server optimizes throughput, latency, and utilization while making it easier to infer all kinds of AI model deployments in production.
NVIDIA AI Enterprise provides Morpheus, RAPIDS, and Triton Inference Server.
Leading Financial Services Companies Use AI
AI is assisting in the fight against the growing trend of online or mobile fraud losses, which are being reported by several major financial institutions in North America.
American Express started using artificial intelligence (AI) to combat fraud in 2010. The company uses fraud detection algorithms to track all client transactions worldwide in real time, producing fraud determinations in a matter of milliseconds. American Express improved model accuracy by using a variety of sophisticated algorithms, one of which used the NVIDIA AI platform, therefore strengthening the organization’s capacity to combat fraud.
Large language models and generative AI are used by the European digital bank Bunq to assist in the detection of fraud and money laundering. With NVIDIA accelerated processing, its AI-powered transaction-monitoring system was able to train models at over 100 times quicker rates.
In March, BNY said that it was the first big bank to implement an NVIDIA DGX SuperPOD with DGX H100 systems. This would aid in the development of solutions that enable use cases such as fraud detection.
In order to improve their financial services apps and help protect their clients’ funds, identities, and digital accounts, systems integrators, software suppliers, and cloud service providers may now include the new NVIDIA AI workflows for fraud detection. NVIDIA Technical Blog post on enhancing fraud detection with GNNs and investigate the NVIDIA AI workflows for fraud detection.
