AI for Fraud Detection

Financial institutions need to detect and prevent sophisticated fraudulent activities, such as identity theft, account takeover, and money laundering. These illicit activities can result in financial losses, reputational damage, and regulatory penalties.

Financial fraud is perpetrated in a growing number of ways, like harvesting hacked data from the dark web for credit card theft, using generative AI for phishing personal information, and laundering money between cryptocurrency, digital wallets, and fiat currencies.

Identifying patterns of financial fraud on a massive scale poses a challenge due to the vast amount of transaction data that must be analyzed rapidly. Additionally, there's a relative scarcity of labeled data for actual instances of fraud, which is essential for training models.

In detecting fraud, banking and payments companies face many challenges including slower process flows, reducing false positives, data integration, and quality, and low-latency thresholds in real-time decision-making.

AI-enabled applications leveraging deep learning techniques such as graph neural networks (GNNs) can reduce false positives in transaction fraud detection, enhance identity verification accuracy for know-your-customer (KYC) requirements, and make anti-money laundering (AML) efforts more effective and thus improve both the customer experience and your company’s financial health.

Financial institutions can develop their own AI capabilities on the NVIDIA AI platform, supporting the entire fraud detection and identity verification pipeline—from data preparation to model training to deployment (inference) by harnessing tools like NVIDIA RAPIDS™ Accelerator for Apache Spark, NVIDIA RAPIDS, and NVIDIA Triton™ Inference Server on NVIDIA AI Enterprise.

NVIDIA RAPIDS for Accelerated Computing

As data needs grow and AI models expand in size, intricacy, and diversity, energy-efficient processing power is becoming more critical to operations in financial services. Traditional data science pipelines lack the necessary acceleration to handle the volumes of data involved in fraud detection, resulting in slower processing times, which limits real-time data analysis and fraud detection. 

To efficiently manage large-scale datasets and deliver real-time performance for AI in production, financial institutions must shift from legacy infrastructure to accelerated computing. The NVIDIA RAPIDS™ Accelerator for Apache Spark, a CUDA-X™ library, which comes as a part of NVIDIA AI Enterprise, uses NVIDIA GPUs to accelerate data processing by up to 5X and reduce costs by up to 4X. NVIDIA RAPIDS supports model training with tree-based algorithms like XGBoost and seamlessly integrates with frameworks like PyTorch/TensorFlow to support deep learning algorithms like GNNs and Transformers.

NVIDIA Triton Inference Server

NVIDIA Triton™ Inference Server provides a powerful and scalable platform for deploying and serving AI-powered models, enabling real-time analysis and detection of fraudulent activities. As part of NVIDIA AI Enterprise, Triton is an open-source inference-serving software used to deploy trained AI models from any framework on any GPU-based infrastructure from cloud to edge. 

NVIDIA® TensorRT™ is a software development kit (SDK) that optimizes trained deep learning models for high-performance inference, allowing fraud detection systems to process data efficiently and make faster decisions without disrupting transaction flow, reducing the risk of financial losses.