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.
A customer relationship management (CRM) system helps you collect, organize and analyze client information. This process allows you to build and maintain relationships with your existing customers and find potential new customers. Many businesses rely on cloud-based platforms, such as Salesforce and Hubspot, to manage their client relations as their businesses grow.