Financial institutions need to detect and prevent complex fraudulent activities, such as identity theft, account takeover, and money laundering. These illegal activities can lead to financial losses, reputational damage, and regulatory penalties.
Financial fraud is being carried out in an increasing number of ways—for example, collecting data hacked by cybercriminals from the dark web to misuse credit cards; using generative AI for phishing to obtain personal information; and converting and transferring funds between cryptocurrencies, digital wallets, and fiat currencies to conduct money laundering.
Identifying large-scale financial fraud patterns is no easy task, as it requires rapid analysis of massive transaction data. Additionally, labeled data on actual fraud cases is relatively scarce, yet such data is critical for training models.
In terms of fraud detection, banks and payment companies face multiple challenges, including slow process execution, minimizing false positives, data integration and quality issues, and low-latency thresholds in real-time decision-making.
AI-powered applications that leverage deep learning technologies like Graph Neural Networks (GNNs) can reduce false positives in transaction fraud detection, improve identity verification accuracy to meet "Know Your Customer" (KYC) requirements, and enhance the effectiveness of Anti-Money Laundering (AML) efforts—thereby improving both customer experience and a company's financial performance.
Financial institutions can develop their own custom AI capabilities on the NVIDIA AI platform. Tools such as NVIDIA RAPIDS™ Accelerator for Apache Spark, NVIDIA RAPIDS, and NVIDIA Triton™ Inference Server (available on NVIDIA AI Enterprise) support the entire fraud detection and identity verification workflow—from data preparation and model training to deployment (inference).
NVIDIA RAPIDS for Accelerated Computing
As data demands grow and AI models expand in scale, complexity, and diversity, efficient processing power has become increasingly critical to financial services operations. Traditional data science workflows lack the necessary acceleration to handle the massive volumes of data involved in fraud detection, leading to prolonged processing times that limit real-time data analysis and fraud detection.
To efficiently manage large-scale datasets and deliver real-time performance for AI applications in production, financial institutions must transition from legacy infrastructure to accelerated computing. As part of NVIDIA AI Enterprise, NVIDIA RAPIDS™ Accelerator for Apache Spark is a CUDA-X™ library that uses NVIDIA GPUs to speed up 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 such as PyTorch/TensorFlow to enable deep learning algorithms like GNNs and Transformers.
