Automation of Mortgage Underwriting Process
Financial ServicesBusiness Impacts
~90%
Classification Accuracy
~85%
Extraction Accuracy
6
Types of Document Classified
Customer Key Facts
- Location : United States
- Industry : Financial Services
- Size : 50 Employees
Problem Context
The customer is a wholesale/correspondent lender. They wanted to classify submission documents and extract required and relevant information for downstream consumption.
Challenges
- Extensive time spent by underwriters on a low value, repetitive work
- High error rate due to manual processing
Document Requirement
Form 1040
IRS W2 Forms
Payslips
Driving License
Passport
Bank Statements
Solution
Quantiphi re-engineered the document underwriting workflow from ingestion and classification to the extraction of mortgage applications. We also added features such as human-in-the-loop review and model retraining to the solution for higher accuracy.
- Ingestion: Simple and robust UI to upload documents
- Document Classification: Classify documents to determine type and content
- Extraction: Extract multiple fields, values, and embedded objects from submission documents
- Human-in-loop: Edit, rename, add and delete documents or extracted values as and when required
- Downstream Connectivity: Output module in compliant MISMO format
- Active Learning and Retraining: Based on the HITL actions and processing of newer documents
Result
- Industry-leading classification and extraction accuracy for underwriting documents
- Streamlining and standardizing of underwriting workflows
