Document Classification & Annotation for Loan Underwriting Automation
Overview:
A leading banking software provider required high-quality annotated datasets to train AI models for automated loan underwriting across multiple lending environments.
Approach:
- Designed custom annotation workflows for diverse financial documents
- Enabled machine-led data extraction with high accuracy and confidence
- Supported scalability across multiple lender implementations
Annotation Workflow:
Document Classification:
Identify and classify document types using visual structure and text patterns.
Includes salary slips, bank statements, tax returns, property, and insurance documents.
NER-Based Annotation:
Perform Named Entity Recognition (NER) to label key textual elements.
Validate and refine machine-generated annotations for accuracy.
Field Extraction:
Annotate critical underwriting fields such as borrower name, income, balances, and property valuation.
Train models to accurately locate and extract decision-relevant data.
Human-in-the-Loop QA:
Cross-verify extracted data against source documents.
Flag exceptions and inconsistencies for iterative model improvement.
Impact:
- Enabled high-confidence automated data extraction for underwriting workflows
- Improved model accuracy through continuous feedback loops
- Built scalable annotation pipelines for multi-lender deployment
- Reduced manual effort in document processing and validation