Financial Data Extraction from Prospectuses
91% Automation with 80% Faster Processing for a Global Bank
The Challenge
A major global bank's asset management division manually processed investment prospectuses — dense, multi-page financial documents containing entities, dates, fee structures, risk disclosures, and regulatory references. The manual extraction process was slow, error-prone, and could not scale with the growing volume of fund onboarding activities.
Our Approach
We developed a custom NLP and deep learning pipeline specifically designed for the structure of investment prospectuses. The system combined document layout understanding with named entity recognition to identify and extract relevant fields: fund names, counterparties, fee percentages, benchmark references, and regulatory clauses.
Model optimization was a continuous process. We used PyTorch and TensorFlow for the core extraction models, with Hugging Face Transformers for pre-trained language understanding. scikit-learn and XGBoost handled classification tasks for field categorization and confidence scoring.
A human-in-the-loop validation workflow was implemented for edge cases. Low-confidence extractions were routed to analysts for review, with corrections fed back into the model through active learning — steadily improving accuracy over time.
The solution was deployed on Azure with MLflow for experiment tracking and model versioning. Close stakeholder collaboration ensured the extraction outputs aligned precisely with the bank's internal data schemas and investment workflows.
The Results
The bank could now onboard more funds without growing its operations team. Analysts previously tied up in manual extraction were redirected to interpretation and decision-making — the work that actually required their expertise — while the pipeline handled the repetitive extraction automatically.
91% Field Automation
80% faster Processing Speed
Production use Stakeholder Fit
MLflow Model Tracking
Technologies Used
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