Automated Regulatory Reporting Platform
90% Manual Effort Reduction in Fintech
[ BUSINESS CONTEXT ]
A European Tier-1 bank employed over 200 analysts solely to manually extract data from disparate internal systems to generate mandatory daily regulatory reports for the European Central Bank (ECB).
[ PROJECT CHALLENGE ]
Human error in these complex reports occasionally resulted in massive fines. The bank required an automated system capable of understanding unstructured financial documents and reconciling them against structured core-banking databases.
[ STRATEGIC SOLUTION ]
We developed an end-to-end automation pipeline. Natural Language Processing (NLP) extracted entities from PDFs, while Robotic Process Automation (RPA) bots pulled data from legacy mainframes, culminating in an automated reporting engine.
Engineering Methodology
NLP Entity Extraction
Trained custom SpaCy models to extract financial instruments and counterparties from unstructured contracts.
RPA Mainframe Integration
Deployed UiPath bots to securely scrape legacy AS400 terminal screens lacking modern APIs.
Data Reconciliation Engine
Built a Python-based rules engine to match extracted data against internal databases and flag discrepancies.
Automated Generation
Generated pixel-perfect XBRL and PDF reports formatted exactly to ECB submission standards.
Quantified Engineering Impact
Report generation time dropped from 8 hours to 45 minutes.
Automation eliminated human transcription errors, drastically reducing regulatory fines.
Highly paid analysts were moved from data entry to strategic risk management roles.
Every data point in the final report included a cryptographic hash linking back to its exact source document.