Automation of Mortgage Underwriting Process

Financial Services
case study

Business 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

Form 1040

IRS W2 Forms

IRS W2 Forms

Payslips

Payslips

Driving License

Driving License

Passport

Passport

Bank Statements

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

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