Objective
To automate the extraction, normalization, and reporting of over 100,000 financial records using Google Gemini AI and Document AI (DocAI). This project transformed raw CDATA-formatted financial data into structured, analyzable formats while integrating a Python-based connector service to update the customer’s CRM with processed insights.
Challenges Faced by the Client
- High Manual Effort & Human Error: The financial data was unstructured CDATA, requiring significant human clerical effort to manually clean and verify reports.
- Scalability Issues: The existing system could not efficiently process large datasets, causing delays and potential data inaccuracies.
- Data Structure Complexity: The incoming data contained nested and inconsistent formats, making it difficult to normalize and analyze.
- Slow CRM Updates: The client’s CRM system lacked a streamlined process for ingesting cleaned and validated financial data, delaying critical business decisions.
Key Features Implemented
Google Gemini & DocAI for Data Extraction & Normalization:
- Used Gemini AI for intelligent data parsing, extracting key financial details from CDATA records.
- Leveraged Google Document AI (DocAI) to process and standardize structured and semi-structured financial documents like invoices, transaction logs, and balance sheets.
AI-Based Data Validation & Error Correction:
- Implemented AI-powered anomaly detection to automatically correct formatting inconsistencies and validate financial records.
Python-Based CRM Connector:
- Developed a real-time API service to push normalized financial data into the customer’s CRM, ensuring seamless reporting and updates.
Batch & Streaming Processing with Google Cloud:
- Integrated Cloud Functions, Pub/Sub, and Dataflow to process financial transactions in real-time with high scalability.
Advanced Financial Reporting with BigQuery & Looker:
- Enabled multi-dimensional financial data analytics with BigQuery & Looker, providing executives with AI-driven insights and forecasting.
Success Criteria & Outcomes
Saved 2,500+ Hours of Manual QA Work
- Eliminated reliance on human clerical teams, significantly increasing operational efficiency.
Improved Data Accuracy by 98%
- Removed manual errors, ensuring high-quality financial data integrity.
Accelerated Financial Reporting
- Reduced data processing time from 48 hours to under 3 minutes per batch.
Seamless CRM Integration
- Automated financial data updates, enabling stakeholders to make real-time, data-driven decisions.
Scalable & Future-Proof Solution
- The system architecture can scale to process millions of records, supporting the client’s long-term financial growth.
This AI-powered DocAI + Gemini financial automation solution has revolutionized the client’s data workflows, setting a new standard for efficiency, accuracy, and scalability in financial data processing