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Letting Data Speak!
Case Study
Automated Bill Data Extraction System
About the Client
A financial services company providing bill payment and account management solutions through their proprietary Finovera API platform, requiring enhanced data extraction capabilities from unstructured bill documents.
Challenge
The client faced significant limitations with their existing bill data extraction system that relied on Finovera API. The system struggled to extract complex textual information such as late fee descriptions and detailed charge descriptions from PDF and image-format bills. Additionally, Finovera API lack of customization capabilities prevented the extraction of client-specific fields and unique metadata, resulting in incomplete data capture for critical billing analysis and downstream processing workflows.
Key Results
Achieved over 95% accuracy rate in automated bill data extraction through LLM-based validation against Finovera API data.
Successfully extracted additional custom fields from bills that were previously unavailable through the existing Finovera system, enriching the dataset for comprehensive analysis.
Reduced processing costs to approximately $30-$40 for processing 3,000 bills through optimized AWS serverless architecture.
Solution
JashDS implemented a comprehensive serverless solution leveraging AWS Bedrock's advanced Large Language Model capabilities to replace the limitations of Finovera API. The team developed a three-Lambda architecture where the first Lambda function processes uploaded bills from S3 buckets using AWS Bedrock for intelligent data extraction, the second Lambda function stores the structured data in Aurora PostgreSQL, and the third Lambda handles validation workflows.
The solution included the creation of a robust database schema with over 20 fields including provider information, account details, payment methods, billing periods, and token usage metrics. A sophisticated SQL-based validation process was implemented to compare LLM-extracted data with existing Finovera API data, calculating column-level matches and percentage scores for accuracy measurement.
The team established an automated workflow triggered by S3 file uploads, ensuring seamless processing of both PDF and image format bills. Advanced error handling, logging mechanisms, and token counting features were integrated to optimize performance and cost management across the entire data extraction pipeline.
Technologies Used
AWS Bedrock (Large Language Models)
AWS Lambda (Serverless Computing)
Amazon S3 (Document Storage)
Amazon Aurora PostgreSQL (Database)
AWS Textract (Initial Implementation)
Amazon DynamoDB (Alternative Storage)
SQL (Data Validation)
Python (Lambda Functions)
AWS RDS (Database Management)
JSON (Data Serialization)
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