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Machine Learning Best Practices in Financial Services

Machine Learning Best Practices in Financial Services

Adoption of artificial intelligence and machine learning (AI/ML) accelerated in recent years. This is due to the availability of cost-effective, virtually unlimited data-storage capacity and compute power from cloud services. Cloud has removed many of the barriers for financial institutions to experiment and innovate with AI/ML. Financial institutions today increasingly build ML models to transform their businesses and prevent fraud. They are also implementing ML workflows to enhance customer experiences, personalize offerings, and optimize trade execution and portfolios. However, in doing so, they face stringent, complex regulatory guidelines.

Based on feedback from customers running workloads in highly regulated environments, our whitepaper outlines security and model governance considerations for financial institutions using AI/ML applications. It illustrates how financial institutions can create a secure machine learning environment on AWS and use best practices in model governance, based on a firm’s risk tolerance, integration with existing governance, and regulatory expectations.

Download this whitepaper and discover how Amazon SageMaker and other AWS services can help multiple stakeholders in your firm build and deploy well-governed and secure ML workloads.

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