Financial losses from worldwide credit card transaction fraud are expected to reach $43 billion by 2026.
A new NVIDIA AI workflow for fraud detection running on Amazon Web Services (AWS) can help combat this burgeoning epidemic - using accelerated data processing and advanced algorithms to improve AI's ability to detect and prevent credit card transaction fraud.
Launched this week at the Money20/20 fintech conference, the workflow enables financial institutions to identify subtle patterns and anomalies in transaction data based on user behavior to improve accuracy and reduce false positives compared with traditional methods.
Users can streamline the migration of their fraud detection workflows from traditional compute to accelerated compute using the NVIDIA AI Enterprise software platform and NVIDIA GPU instances.
Businesses embracing comprehensive machine learning tools and strategies can observe up to an estimated 40% improvement in fraud detection accuracy, boosting their ability to identify and stop fraudsters faster and mitigate harm.
As such, leading financial organizations like American Express and Capital One have been using AI to build proprietary solutions that mitigate fraud and enhance customer protection.
The new NVIDIA workflow accelerates data processing, model training and inference, and demonstrates how these components can be wrapped into a single, easy-to-use software offering, powered by NVIDIA AI.
Currently optimized for credit card transaction fraud, the workflow could be adapted for use cases such as new account fraud, account takeover and money laundering.
Accelerated Computing for Fraud Detection As AI models expand in size, intricacy and diversity, it's more important than ever for organizations across industries - including financial services - to harness cost- and energy-efficient computing power.
Traditional data science pipelines lack the necessary compute acceleration to handle the massive volumes of data required to effectively fight fraud amid rapidly growing losses across the industry. Leveraging NVIDIA RAPIDS Accelerator for Apache Spark could help payment companies reduce data processing times and save on their data processing costs.
To efficiently manage large-scale datasets and deliver real-time AI performance with complex AI models, financial institutions are turning to NVIDIA's AI and accelerated computing platforms.
The use of gradient-boosted decision trees - a type of machine learning algorithm - tapping into libraries such as XGBoost, has long been the standard for fraud detection.
The new NVIDIA AI workflow for fraud detection enhances XGBoost using the NVIDIA RAPIDS suite of AI libraries with graph neural network (GNN) embeddings as additional features to help reduce false positives.
The GNN embeddings are fed into XGBoost to create and train a model that can then be orchestrated with the NVIDIA Morpheus Runtime Core library and NVIDIA Triton Inference Server for real-time inferencing.
The NVIDIA Morpheus framework securely inspects and classifies all incoming data, tagging it with patterns and flagging potentially suspicious activity. NVIDIA Triton Inference Server simplifies inference of all types of AI model deployments in production, while optimizing throughput, latency and utilization.
NVIDIA Morpheus, RAPIDS and Triton Inference Server are available through NVIDIA AI Enterprise.
Leading Financial Services Organizations Adopt AI During a time when many large North American financial institutions are reporting online or mobile fraud losses continue to increase, AI is helping to combat this trend.
American Express, which began using AI to fight fraud in 2010, leverages fraud detection algorithms to monitor all customer transactions globally in real time, generating fraud decisions in just milliseconds. Using a combination of advanced algorithms, one of which tapped into the NVIDIA AI platform, American Express enhanced model accuracy, advancing the company's ability to better fight fraud.
European digital bank bunq uses generative AI and large language models to help detect fraud and money laundering. Its AI-powered transaction-monitoring system achieved nearly 100x faster model training speeds with NVIDIA accelerated computing.
BNY announced in March that it became the first major bank to deploy an NVIDIA DGX SuperPOD with DGX H100 systems, which will help build solutions that support fraud detection and other use cases.
And now, systems integrators, software vendors and cloud service providers can integrate the new NVIDIA AI workflow for fraud detection to boost their financial services applications and help keep customers' money, identities and digital accounts safe.
Explore the fraud detection NVIDIA AI workflow and read this NVIDIA Technical Blog on supercharging fraud detection with GNNs.
Learn more about AI for fraud detection by visiting the NVIDIA AI Pavilion featuring AWS at Money 20/20, running this week in Las Vegas.
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Financial losses from worldwide credit card transaction fraud are expected to reach $43 billion by 2026.
