The Dynamics Underlying Fully Automated Credit Application Processing

What you’ll learn

  • Understand how credit applicants enable easy information extraction.
  • Understand how automated Credit Application help in faster credit approval.
  • Learn how automation trumps over manual credit application.


Credit applications are cumbersome. Their complexity and the chance information will be incomplete conspire to create a lengthy process.  Paper applications are the biggest offenders as they have to be sent, returned and then manually processed with the inevitable back-and-forth to gather any missed information or signatures, and that’s before any financial analysis has been performed.

Once processed, credit applications are typically archived either in a file cabinet or as a digital document image.  When applications are available online, they still are often stored as a PDF file.  But so what? Archived credit applications are indexed to the customer account along with a credit bureau report and other documents, but the information is not shared with any systems. A credit application that is merely archived is as static as a paper document. Such a situation is begging for automation.

With an automated online credit application, contact information and other demographics are electronically extracted directly from the application and used to populate Customer Master File data fields.  This automated step by itself benefits other processes when there is integration to CRM or AR software.  Other information needed for credit scoring models, such as year founded, the number of employees, financial status, and payment trends is extracted automatically either from the application, a credit bureau or from public online sources. All this is a step in the right direction, but there is still more automation that can take place.

When credit bureau data elements are captured with automated interfaces they can be used to speed up the validation when an application is being completed. Besides verifying the applicant is who they say they are, key demographic data is pulled from the application and crosschecked with the credit bureau as part of the validation process and to help populate additional open fields on the application.

Because the interface can automatically pull data from various sources, the data required from the customer on the application can be further enriched.  This data can then automatically be added to the master data files.  In addition, corporate linkages (family tree) can be captured from the credit bureaus and used to identify other related customers.  By using the power of the web to its fullest, especially with public companies, a wealth of information can be found to aid the credit application process.

Financial statements can also be submitted with an online credit application (or sent as a document image and converted into an electronic format by the automated interface using OCR technology) or captured from SEC filings if it’s a public firm.  Skipping the need for manual data entry, the process is sped up further because the automated credit solution goes a step beyond by generating ratios and period-to-period comparisons that can be inputted into the credit scoring models.

All this data – either automatically pulled out of the application or uploaded from credit bureaus and other public sources – is used to drive automated decisions.   Approvals and denials can be determined within pre-set parameters without the need for manual touches.  Even decisions that aren’t so cut and dry can be summarized in a report format in order to provided support for the human decision-makers, with the system ensuring appropriate approval authorities.  After the application is approved, an automated solution can then go the extra mile by generating new customer documents including approval letters, contracts, leases, security agreements, and so forth.

The more data you capture and support by automated workflow, the shorter your approval process will be.  The ultimate goal is to then drive as many automated decisions as possible while reducing the number of manual interventions.

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