Optimizing loan applications processing with banking document OCR

Case study / Banking / OCR / AI / Accounting/ Fintech

The OCR bank document workflow allowing to optimize filling loan applications by autoextracting data that will result in reducing input errors and approving loans in seconds.


According to Banque de France, “at the end of May 2020, the outstanding amount of drawn credit reaches 1,140.3 billion euros, up by +10.8% year-on-year, after a rise of +8.2% between April 2019 and April 2020.” This statement shows us clearly that the “loan industry” is still in growth. And this is not a surprise when we know that a loan can be made for a lot of reasons: real estate, car, studies, etc. Nevertheless, this transaction can be truly time consuming and difficult. In this use case, we will see how banking document OCR technology can improve this process.


  • Extract data from scanned personal (ID card, passport), bank (bank statement), accounting (invoice/bill, receipt), legal documents (KBIS), and customs documents (contracts, reports, etc.)

  • Speed up screening and sorting application documents

  • Streamline auditing and reporting

  • Simplify compliance

  • Structure collected information to review and analyze problem cases


  • Connect banking document OCR API to company software

  • Train software using ML algorithms

  • Recognize, collect, and classify information over APIs

  • Enable converting data and storing in databases


At the end of this use case, we can observe that the data are being extracted in seconds through auto-filling with facilitated scanning and classifying banking documents. Compliance checks and structuring data are provided so problem cases may now be quickly reviewed and analyzed. Workflows and data searching are optimized to ensure better user experience. And finally, loan applications can be fulfilled and processed in-the-blink of an eye.

If you are interested in the automated processing of loan applications, at Young App, we are helping companies with banking document OCR projects.

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