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AWS Solutions Library

Guidance for Predicting Loan Defaults for Financial Institutions on AWS

Overview

This Guidance helps financial institutions use AWS services for automated machine learning (ML) to predict loan defaults with minimal effort. Bad loans can have adverse effects on a bank’s net financial performance and lending potential. Using serverless and ML services, business analysts can quickly determine loan risks without high costs or the need to build code. This results in proactive credit risk management, mitigation of credit risks, profit maximization, and improved regulatory compliance. 

How it works

This architecture shows how to predict loan defaults using AWS AutoML and serverless technology.

Download the architecture diagram

Well-Architected Pillars

The architecture diagram above is an example of a Solution created with Well-Architected best practices in mind. To be fully Well-Architected, you should follow as many Well-Architected best practices as possible.

This Guidance uses Amazon AppFlow, a fully managed integration service that helps you securely transfer data between different services. This Guidance also uses SageMaker Data Wrangler, SageMaker Autopilot, and cloud-native support and integration for Amazon S3, facilitating the preparation and transformation of your dataset. With SageMaker Autopilot, you can retrain and deploy your model with updated datasets as needed.

Read the Operational Excellence whitepaper 

This Guidance requires an AWS Identity and Access Management (IAM) account, which restricts access and permissions to the minimum required permissions for the service to function. Additionally, this Guidance has server-side encryption through either Amazon S3 or AWS Key Management Service (AWS KMS).

Read the Security whitepaper 

This Guidance supports durable storage through Amazon S3 and automatic scaling through SageMaker. You can also monitor SageMaker through CloudWatch, which converts raw data into readable metrics in near real time and sets alarms for when you reach thresholds.

Read the Reliability whitepaper 

This Guidance uses SageMaker Autopilot, which can generate notebooks to manage multiple automatic-ML jobs and experiments. You can edit these notebooks as needed, and features like explainability help you better understand the model.

Read the Performance Efficiency whitepaper 

This Guidance uses serverless services such as Lambda and services that scale to match demand, such as SageMaker Autopilot, Amazon RDS, Amazon Redshift, and Amazon S3, so you only pay for the resources you need. You can also choose between on-demand pricing, a savings plan, or a combination of the two for further cost savings.

Read the Cost Optimization whitepaper 

This Guidance uses Amazon SageMaker Model Monitor, which can automate your model drift detection, thereby reducing resource usage.

Read the Sustainability whitepaper 

Disclaimer

The sample code; software libraries; command line tools; proofs of concept; templates; or other related technology (including any of the foregoing that are provided by our personnel) is provided to you as AWS Content under the AWS Customer Agreement, or the relevant written agreement between you and AWS (whichever applies). You should not use this AWS Content in your production accounts, or on production or other critical data. You are responsible for testing, securing, and optimizing the AWS Content, such as sample code, as appropriate for production grade use based on your specific quality control practices and standards. Deploying AWS Content may incur AWS charges for creating or using AWS chargeable resources, such as running Amazon EC2 instances or using Amazon S3 storage.