In the evolving world of digital banking, detecting cheque fraud efficiently is a critical need. Financial institutions today process trillions of transactions, with fraud-related losses reaching over $31 billion annually, according to McKinsey. With the increasing sophistication of fraud schemes, advanced AI solutions are necessary for robust risk management.
At Hexaware Techno, we developed an intelligent AI-powered fraud detection system using AWS cloud services. This solution streamlines cheque processing and ensures rapid, accurate fraud detection using Amazon SageMaker, Amazon Bedrock, Snowflake, and a series of serverless AWS services.
Why Rethink Cheque Processing?
Manual or semi-automated cheque processing often fails to meet the needs of high-volume, real-time fraud detection. The solution we built focuses on:
Real-time data ingestion and document analysis
Scalability with serverless and containerized compute resources
Fraud prediction using machine learning models
Seamless analytics integration for business insights
Building Hexaware’s AI-Powered Fraud Detection Pipeline
Leveraging AWS’s cloud-native services, we created an automated, scalable cheque fraud detection system. The workflow includes:
Amazon S3 & EventBridge: For automatic ingestion and event handling
Amazon Bedrock: To process cheque images intelligently and extract relevant data
DynamoDB & Streams: For storing and managing metadata
Amazon SageMaker: For fraud risk analysis
ECS Fargate: To handle heavy compute tasks like batch processing
Step-by-Step Architecture
ICL File Upload & Event Trigger:
Cheque files are uploaded to Amazon S3.
EventBridge triggers the initial processing task in ECS.
ICL File Processing:
The ECS task extracts images and metadata from cheque files, storing them in Amazon S3 and DynamoDB.
Image Analysis with Amazon Bedrock:
Images are processed using Amazon Bedrock to extract structured data like cheque amounts, dates, and signatures.
The extracted data is saved into DynamoDB.
Metadata Updates and Conditional Progression:
DynamoDB Streams monitor metadata updates.
When processing is complete, the system triggers the next step, invoking the Prediction Processor.
Snowflake Integration:
Processed metadata is imported into Snowflake for deep analytical capabilities and reporting.
Fraud Prediction with SageMaker:
The cheque images are converted to Base64 and sent to Amazon SageMaker for fraud analysis.
Results are stored back in S3, including the fraud risk scores and probabilities.
Final Processing & Reporting:
The final ECS task processes prediction results, stores them in DynamoDB, and imports them into Snowflake for reporting and analysis.
Key Features of the AI-Powered Fraud Detection Solution
Full Automation: End-to-end orchestration eliminates manual processing and reduces human error.
Real-Time Data Handling: Event-driven architecture ensures that fraud detection occurs instantly.
AI-Driven Analysis: Amazon Bedrock and SageMaker provide the intelligence to extract relevant data and predict fraud.
Scalability: The solution is designed to handle millions of cheques efficiently without compromising speed.
Analytics-Ready: Data is stored in Snowflake, ready for actionable business insights.
Great news!