- Inefficiencies in document verification to classify bank statements, paystubs, and tax forms for loan approvals and risk underwriting.
- Data dispersed across different mortgage data applications often hindering the ability to quickly scan documents of interest.
- Lack of data science depth to train highly efficient algorithms on dispersed datasets limited the accuracy of document categorization.
- Devron deployed across multiple loan data applications that housed sensitive documents (e.g., bank statements, tax-ids, W-2s) instead of duplicating and centralizing data.
- Devron Central Authority trains algorithms to understand each type of document using computer vision and high accuracy image classification techniques.
- With more training data and time saved from unnecessary centralization, Devron predicts loan approval and risk underwriting documents of interest with over 95% accuracy.
Increase Document Management Efficiency
Greater Business Insights
Increased Model Accuracy
Reduced Data Centralization Time & Costs