Case Study

A Financial Services Company leverages MLOps to Secure Data Processing, Improved Fraud Detection Techniques, and Smart Decision-Making

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Financial Services


Amazon SageMaker, AWS CodePipeline, AWS CodeBuild, AWS CodeCommit, Amazon S3, and AWS CloudWatch


Highly scalable and secure AI solutions on AWS with Real-time fraud detection, automated pipelines, and continuous model retraining for optimal performance

About the Client

Credit Saison is a Japanese financial services company. They offer a platform connecting users with banks and financial institutions for multiple loan products with customized terms, amounts, and repayment options. They also provide lines of credit to consumers via the application. 


15 minutes

Data Processing and Pipeline Execution

Easy Deployments using AWS CloudFormation Templates

AWS Developer Tools

Reduced Business Risk

Real-time fraud detection

The Challenge

Real-time fraud detection, risk management, debugging metrics, and model deployment issues were hampering business. They want to migrate to AWS for a streamlined ML pipeline, automatic model retraining, and improved scalability to enhance response times and reduce risk exposure. 


  • The dataset uploaded to Amazon S3 triggers Event Bridge for task triggers in the pipeline.  
  • XML dataset parsed using Amazon SageMaker Pipeline with XML Parsing step. 
  • Amazon SageMaker Pipelines manages the machine learning lifecycle, Amazon SageMaker Processing steps used for feature engineering, and EDA and Amazon SageMaker Training steps used for real-time and batch model training. 
  • Trained model artifacts are automatically stored in Amazon S3 and registered in the model registry. 
  • Amazon SageMaker Evaluation generates metrics for classification and regression. 
  • Approved model deployed in real-time applications using Amazon SageMaker Endpoint. 
  • Architecture automated with AWS CloudFormation templates and CI/CD tools for pipeline rebuilding. 
  • CI/CD pipeline with 2 Code Pipelines integrating GitHub and AWS CodeCommit repositories. 

The Results

Maximum performance, minimal financial losses, and decisive results with scalable data storage, automated 15-minute pipeline for model training and preprocessing, and real-time fraud detection. 

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