AWS SageMaker is a powerful tool for financial crime analytics, allowing financial institutions to quickly and easily build, train, and deploy machine learning (ML) models for fraud detection and prevention.
With SageMaker, financial institutions streamline the entire process of developing a machine learning model, by quickly and easily importing and preprocessing data, training and optimising models, and deploying them at scale. This can significantly reduce the time and resources required to build and deploy a robust fraud detection system.
Another benefit of using SageMaker is its ability to handle large volumes of data. Financial crime analytics require the processing of large datasets, which can be a daunting task for traditional machine learning systems. However, SageMaker provides powerful tools for handling large datasets, including automatic data partitioning and distributed training, which allows for more efficient model training and faster time-to-insight.
SageMaker also provides a wide range of machine learning algorithms and frameworks, allowing financial institutions to select the ones that best fit their specific needs. These include supervised and unsupervised learning algorithms, as well as deep learning frameworks such as TensorFlow and PyTorch.
Another advantage of using SageMaker for financial crime analytics is its ability to scale and automate the deployment of machine learning models. SageMaker enables financial institutions to deploy models as RESTful APIs, which can be easily integrated into their existing fraud detection systems. This can significantly reduce the time and effort required to deploy and manage models, and can help institutions quickly adapt to new fraud threats.
Business Use Case
A couple of years ago, a financial institution’s transaction monitoring program indicated that the level of maturity was low, with low coverage of risk factors and a high degree of inefficiency. The assessment also recommended the development of a financial crime specific data analytics capability to supplement automated detection and enable exploratory analysis in a non-production environment.
The ability to trial new detection scenarios in a test environment, to test hypotheses, and to understand the impacts of adjustments to thresholds on the quality and volume of existing detection scenarios, is highly valuable in the management of financial crime risk across the organisation.
The setup of a dedicated analytics environment will enable the data analytics team to facilitate scenario exploration and evaluation, and red flag risk impact assessments in a timely efficient manner without impacting financial crime operations or requiring vendor implementation.
The main beneficiary of the change will be financial crime operations who will improve the ability to respond to changing financial crime risk faster and reduce their operational risk. They will also benefit from tighter controls and more visibility of the risk exposure in relation to customer monitoring. The data analytics team will be enabled to provide more support to both these chapters in their business as usual activities.
MLOps Platform Ecosystem
The following extensions on this MLOps platform ecosystem included:
- Integration of Apache Airflow to orchestrate the data engineering pipeline.
- Integration with S3 data lake to store and access data, and ensure privacy.
- Provision for data scientists and ML developers to develop models on Jupyter Notebooks and Anaconda packages.
- ML pipelines to set up a batch transform service to perform predictions against an entire dataset.
- Integration with AWS EMR for the Spark execution to query data and use of Spark to build transformations for analytics with enough computation power to run machine learning models iteratively.
- DBT for ETL to be executed on Spark clusters as a pipeline and automate regularly used data transformations, and receive notifications based on the outcome.
- A development environment with Python and R with a set of libraries to do analytics, so that a data science developer can write and execute analytics code using both languages.
- Redshift for data warehousing which synchronises with the output S3 bucket.
- Connect Power BI to data sources which contain analytical assets to prepare visualisations, dashboards and reports.
- Model management to update model versions to ensure models are retrained and aligned to changes in the data ecosystem.
- Analyst is able to store and manage training and test data sets to test models against specific versions of training data and test data to directly compare model performance.
Key Goals
- Development and promotion of ML models to production
- Operationalisation of AI/ML workloads and workflows
- Creation of secured, automated, and reproducible ML workflows
- Management of models with a model registry and data lineage
- Enablement of continuous delivery with IaC and CI/CD pipelines
- Performance monitoring and feedback information to models
- Provision of compliance, security, and cost tools for ML development
- Increased collaboration and experimentation
SageMaker Solutions Architecture
- Amazon SageMaker Studio provides a single, web-based visual interface where you can perform all ML development steps, improving data science team productivity by up to 10x.
- Sagemaker Studio is a fully managed service.
- Sagemaker Studio provides a RBAC based control that can be easily implemented and controlled based on individual organisation’s guardrails.
- SageMaker Studio provides complete access control and visibility into each step required to build, train, and deploy models.
- Sagemaker Studio will manage and control AWS Services that get provisioned via Autopilot and Model experiments in a controlled manner with instance types denied and restricted.
- The deployment and provisioning of Sagemaker Studio Domain, user profiles and necessary IAM roles and policies are created using Cloudformation templates and Jenkins jobs for automation.
- It allows organisations to integrate with AD SSO for authentication and IAM roles/policies are used to control authentication and authorisation.
Outcomes
- The outcome of using an MLops platform like AWS SageMaker is a streamlined and efficient process for developing, deploying, and managing machine learning models at scale.
- This can result in faster time-to-insight, improved accuracy and performance of models, and a reduction in the time and resources required for model development and deployment.
- Ultimately, the goal of using an MLOps platform is to improve the effectiveness and efficiency of machine learning models in solving real-world problems, such as detecting and preventing financial crime.
Overall, AWS SageMaker provides financial institutions with a powerful and flexible platform for developing and deploying machine learning models for financial crime analytics. With its powerful tools for data preprocessing, model training, and deployment, SageMaker can help institutions quickly and effectively detect and prevent fraud, protecting both their bottom line and their customers.
