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Fraud Detection AI Workflow | NVIDIA

Solutions: AI Workflows

Fraud Detection With AI

Detect and prevent sophisticated fraudulent activities in payments with high accuracy and reduced false positives.

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    Boost the Precision of Fraud Detection With AI

    Transaction fraud detection is a $43B problem annually and poses a big challenge for financial institutions to detect and prevent sophisticated fraudulent activities. With NVIDIA’s fraud detection AI workflow, enterprises can augment their fraud detection using deep learning techniques including graph neural networks (GNNs), allowing for real-time analysis and improved accuracy.

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    Explore the Fraud Detection AI Workflow

    The fraud detection AI workflow example demonstrates how the GNN model-building process can be run, the frequency of which is dependent on model training latency.

    This Workflow Contains

    • A reference solution for the credit card fraud detection use case for a large financial institution that leverages the NVIDIA Triton™ Inference Server and trains models on an NVIDIA platform.
    • Orchestration using the NVIDIA Morpheus SDK, which accelerates massive data processing and analysis and helps with inferencing.
    • The model-building process, leveraging GNN training to produce features to be fed into an XGBoost model for training.
    • Sample Jupyter Notebooks for helping you adapt this solution to your data and business.

    Given how dynamic the consumer finance industry is with evolving fraud trends, financial institutions that train models adaptively on a frequent basis tend to have better fraud prevention KPIs as compared to their competitors.

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    Fast-Track Your Fraud Detection Journey With a Free Trial on NVIDIA LaunchPad

    Get immediate access to the fraud detection workflow with a free curated lab. Access a step-by-step guided lab with ready-to-use software, sample data, and applications.

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    Reduce Risk With Fraud Detection

    Financial institutions can improve fraud detection by utilizing the NVIDIA AI workflow. Leveraging tools available on NVIDIA AI Enterprise allows fraud detection systems to process data efficiently, make faster decisions without disrupting transaction flow, and reduce the risk of financial losses.

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    Improve Your Company’s Financial Health

    Reduce False Positives

    Leverage deep learning techniques such as GNNs to reduce false positives in transaction fraud detection.

    Deliver Real-Time Analysis and Make Faster Decisions

    Optimize trained deep learning models for high-performance inference to process data efficiently and make faster decisions without disrupting transaction flow.

    Improve Accuracy

    Monitor the performance of deployed models to detect changes in fraud patterns and make accurate decisions.

    Accelerate the Development of AI Solutions

    AI workflows accelerate the path to AI outcomes. The fraud detection workflow gives developers a reference solution to start building applications that can improve the customer experience.

    Reduce Development Time

    Best-in-class AI software streamlines development and deployment of AI solutions.

    Improve Accuracy and Performance

    Frameworks and containers are performance-tuned and tested for NVIDIA GPUs.

    Speed Time to Deployment

    Prepackaged, customizable reference applications with cloud-native deployable packaging.

    Gain Confidence in AI Outcomes

    Business-critical AI projects stay on track with NVIDIA Enterprise Support, available globally.

    Resources

    Read the Technical Blog

    In this blog, we walk through how you can get started with model building and inference through the fraud detection workflow.

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    Explore the Use Case

    Learn more about how banks, payments companies, and fintechs can use AI for fraud prevention, such as reducing false positives in transaction fraud detection or enhancing identity verification accuracy for know-your-customer (KYC) requirements.

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