Financial Document Categorization

A financial institution needed to isolate relevant intelligence from various data types amid a sea of information.


  • 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.
Financial Document Categorization
Financial Document Categorization
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