AI for Fraud Detection

AI for Fraud Detection

Boost the precision of fraud detection for better risk management and increased customer retention.

Workloads

Data Science

Industries

Financial Services

Business Goal

Risk Mitigation

Products

NVIDIA AI Enterprise
NVIDIA RAPIDS
NVIDIA Morpheus

Faster Fraud Detection Reduces Risk

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-Driven Fraud Prevention

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.

“Our fraud algorithms monitor, in real time, every American Express transaction around the world for more than $1.2 trillion spent annually, and we generate fraud decisions in mere milliseconds. Having our card members’ and merchants’ backs is our top priority, so keeping our fraud rates low is key to achieving that goal. Especially in this environment, our customers need us now more than ever, so we’re supporting them with best-in-class protection and servicing.“

VP of Machine Learning and Data Science
American Express

Combating Fraud With NVIDIA’s AI Platform

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.

Getting Started With AI for Fraud Detection

Financial institutions looking to build fraud detection workflows can employ NVIDIA AI Enterprise, an end-to-end, cloud-native software platform that accelerates data science pipelines and streamlines development and deployment of production-grade AI applications. Here are the four distinct steps:

  1. Data Preparation: Collect relevant data such as transaction logs, customer profiles, and historical fraud records. Clean and preprocess the data, handle missing values, outliers, and perform feature engineering. 

  2. Model Training: Select appropriate machine learning algorithms such as XGBoost, random forest, or neural networks. Train the models using the preprocessed data, splitting it into training and validation sets. Evaluate the model's performance using metrics like accuracy, precision, recall, and F1 score. 

  3. Model Deployment: Deploy the trained models using NVIDIA Triton Inference Server for real-time inference. Integrate the models into the banking or payment system, ensuring scalability and reliability. Implement post-processing techniques for making final decisions on blocking or allowing transactions. 

  4. Monitoring and Improvement: Continuously monitor the performance of the deployed models, detecting changes in fraud patterns or model drift. Collect feedback on model predictions and outcomes to improve accuracy and adapt to evolving fraud techniques. Update and retrain the models periodically to maintain effectiveness and stay ahead of fraudsters.

Enterprises that run their businesses on AI rely on the security, support, and stability provided by NVIDIA AI Enterprise to ensure a smooth transition from pilot to production.

Migrating from machine learning to deep learning for fraud detection can have significant business impacts. Deep learning models offer improved accuracy in detecting fraudulent activities, enabling real-time detection, and reducing false positives. These models are highly scalable and can handle large volumes of transaction data efficiently. 

Deep learning techniques can capture complex fraud schemes that involve multiple transactions over time. By automating and streamlining the fraud detection process, businesses can achieve cost savings and operational efficiency. While this migration may require additional computational resources, the benefits of improved accuracy and real-time detection make it a valuable investment for businesses.

American Express deployed deep learning models optimized with NVIDIA TensorRT and running on NVIDIA Triton™ Inference Server to detect fraud. Their fraud algorithms monitor every transaction around the world in real time, and they generate fraud decisions in mere milliseconds, resulting an improved fraud detection accuracy by six percent.

AI, data science, and machine learning models can be used to detect anomalies in customer behaviors, patterns of accounts, and behaviors that fit fraudulent characteristics. Consider leveraging AI technologies to enhance fraud detection capabilities.

Embrace identity verification technologies: AI-driven applications using deep learning techniques and natural language processing (NLP) can enhance identity verification processes, leading to improved regulatory compliance and reduced costs.

Leverage tree-based models for fraud detection: Tree-based models, such as XGBoost, LightGBM, and Random Forest, can be deployed using the Forest Inference Library (FIL) backend in the NVIDIA Triton Inference Server. These models can provide accurate fraud detection capabilities with low latency and high throughput.

Stay updated on the latest fraud detection techniques: Keep abreast of advancements in fraud detection technologies and methodologies. Attend industry conferences, webinars, and training sessions to stay informed about the latest trends and best practices in fraud prevention.

Collaborate with industry partners: Engage with independent software vendors (ISVs), global service integrators (GSIs), and service delivery partners (SDPs) to share insights and best practices for fraud prevention. Partners in the ecosystem can help integrate effective technology solutions for fraud prevention in your business.

AI for fraud detection is highly scalable and can be effectively implemented in businesses of all sizes. With the ability to handle large volumes of data and process it in real time, AI models can keep pace with the growing demands of financial institutions. 

Cloud infrastructure provides flexible resources for deploying and managing fraud detection models, allowing you to scale up or down based on your needs. 

Automation and efficiency enable businesses to scale their fraud detection operations without significantly increasing the workforce required. AI models can be continuously trained and adapted to evolving fraud patterns, ensuring scalability and responsiveness to changing fraud trends. 

Integration with existing systems allows for seamless scalability without major disruptions. Overall, AI for fraud detection offers scalable solutions that can meet the growing needs of your business while effectively mitigating fraud risks.

Protect Your Institution and Your Customers From Fraud

Financial institutions can reduce false positives in transaction fraud detection, enhance identity verification for KYC requirements, and make AML more effective, improving both the customer experience and your company’s financial health with NVIDIA’s AI platform.

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