Artificial intelligence and machine learning - Financial Services Industry Lens

Artificial intelligence and machine learning

Financial institutions have used artificial intelligence and machine learning (AI/ML) technologies for years. Today, financial services organizations are harnessing the power of AI/ML to improve surveillance, reduce fraud, mitigate risk and improve compliance, enhance customer interactions, and improve operational efficiency.

Characteristics of AI/ML applications in the financial services domain

Integration of AI/ ML technologies into day-to-day operations has advanced slowly due to a lack of in-house data science and machine learning operations (MLOps) expertise and insufficient tools and services orchestrating these complex workflows. AWS provides a set of tools that make AI/ML readily accessible to any organization. Financial institutions have the following common design requirements in order to make AI/ML workloads successful in their organizations:

  • Secure ML environment: Financial institutions have stringent security requirements for several reasons, including data protection, regulatory compliance, prevention of adversarial exploits, and to maintain trust and responsible use of AI.

  • Self-service ML capabilities: Customers using these AWS services can enable both technical and non-technical domain experts to employ Machine Learning to foster a culture of data-driven decision-making throughout the organization.

  • Continuous integration and delivery (CI/CD): Automate the deployment process to make it easier to roll out models into production environments and provide version control models and code artifacts.

  • Monitor ML models: CI/CD pipelines enable continuous monitoring of deployed models, allowing teams to gather feedback, verify auditability, track performance, and make necessary adjustments.

Reference architecture

Artificial intelligence and machine learning reference architecture diagram

Figure 2: Reference architecture for an AI/ML pipeline

AI/ML architecture description

Business requirements phase: Define the functional requirements of the workload identifying the business problem and the desired outcomes of the AI system. Then frame the business problem by analyzing what the AI/ML application solves, what behaviors are observed, and what information should be predicted.

ML infrastructure phase: Integrate Amazon SageMaker AI with AWS networking and security services including Amazon Virtual Private Cloud (Amazon VPC), Identity and access management (IAM), AWS Key Management Service (KMS) to provide a secure environment for end-to-end machine learning workloads.

Continuous Integration Phase: SageMaker AI facilitates CI/CD by providing features like SageMaker AI Pipelines and SageMaker AI Studio. SageMaker AI Projects allows the MLOps teams to create a standardized ML experimentation environment, leverage libraries, source control repositories, and CI/CD pipelines. Data scientists can take advantage of AWS services like CodeBuild and CodeDeploy to automate the following workflows:

  1. Data preparation workflow: Data is collected and cleaned, removing inconsistencies or errors. Then, features are selected or engineered, and the data is split into training and testing sets, to provide quality and suitability of the data for the machine learning model.

  1. Model Build Workflow: The model build and evaluation workflow in machine learning involves two main steps. First, a model is built using a training dataset, using SageMaker AI Training, where the algorithm learns patterns and relationships from the data. Then, the model's performance is evaluated using a separate testing dataset to assess its predictive capabilities and generalization to new and unseen data. Customers can use SageMaker AI HPO for hyper-parameter tuning in complex machine learning systems, such as deep learning neural networks, enhancing productivity by systematically exploring various combinations of hyperparameter values within specified ranges to automatically identify the best model.

Continuous delivery phase: SageMaker AI MLOps capability automates deploying and delivering machine learning models into production in a consistent manner. ML operations teams can leverage AWS continuous integration capabilities using AWS CloudFormation, CodeBuild, and CodeDeploy to automate model deployment workflows. Amazon SageMaker AI model monitoring allows customers to monitor ML applications for potential data drifts, model drifts, and bias drifts.

Model performance evaluation - evaluate the performance and accuracy of the machine learning model. Feed model drift and errors back into the model to correct it and generate more precise inferences.

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