How to Use AI to Digitize Loan Processing – A Brief Overview

Many large banks and financial institutions are starting to digitize parts of their business processes to prepare for future automation and machine learning initiatives. This is especially true with loan processing. These functions could become faster and more accurate if they use more easily accessible scanned data than paper documents.

In this article, we’ll explain how to effectively use AI to digitize loan processing. Our guide covers the following topics:

  • The problem with paper documents: For example, maintaining regulatory compliance by keeping customer data private, but accessible to those who need access for legal reasons.
  • How AI could facilitate loan processing: Use methods such as document scanning and text mining to digitize the loan processing workflow.
  • Challenges in adopting document scanning solutions: Including the need to tag digitized loan documents with metadata.

We begin our guide on using AI to digitize loan processing with an overview of the challenges paper documents can cause in this area:

The paper problem in loan processing

Many banks and financial institutions still use paper documents for loans and other basic business processes. This can lead to problems that slow down the processing of loans in different business areas. These businesses face the following challenges in continuing to use paper documents:

  • Maintaining regulatory compliance
  • Slow turnaround time
  • Disorganized underwriting of loan and mortgage documents

In order to stay in compliance with government regulations such as the GDPR or the California Consumer Privacy Act, businesses must keep all of their customer data accessible and traceable. If a customer asks a bank to delete all the information they hold about them, the bank might have difficulty meeting and proving that request.

In most cases, the company wouldn’t be able to track down every physical document containing the customer’s information because they hadn’t tracked every page for years. This puts the bank at risk of non-compliance, which can lead to fines of several million dollars.

Paper documents can also slow down loan origination time. Once a client is eligible for a loan, they must provide full documentation to prove their income. This includes the purchase and sale contract, W-2 forms and bank statements. If refinancing an existing mortgage, the customer will also need to provide mortgage statements.

Lenders who have to work with paper documents will take longer to issue loans as they spend more time flipping through the pages and manually referencing each document. If one or more of these documents is misplaced, it may take a full business day or more to move it before the loan can be completed.

How to facilitate loan processing functions with AI

Each of the business processes discussed previously requires some form of research into company data – a task that requires data processing and harmonization beforehand (which often takes much longer than business leaders do. think so).

AI solutions could ease the research process and speed up loan underwriting and origination by digitizing all physical documents, making the corporate intranet more accessible and easier to use.

Two AI capabilities, in particular, could make such improvements possible:

  • Document scanning
  • Text mining and automated metadata tagging

Digitization of customer data stored in paper documents

AI document scanning applications require machine vision technology equipped with a camera capable of producing high resolution images of paper documents to accurately convey the information they contain. In addition, optical character recognition (OCR) can also reproduce the written word in a digital format. The new digital version of a document can then be saved in the company’s database.

Banks could benefit from using machine vision to scan documents needed for loans and underwriting, such as:

  • Purchase and sale contracts
  • Paper or scanned PDF applications for mortgages.
  • Physical and scanned PDF checks
  • Bank statements for customers proving their banking and credit history

Wealth managers could see their productivity increase if all of these types of documents could be digitized. Indeed, all the data necessary to qualify and integrate new loan clients would be accessible on the bank’s intranet. They wouldn’t need to leave their desks or search through filing cabinets to reference the data needed to make important business decisions like an applicant’s credit history. That said, newly digitized data should still be easily searchable for this method to be as effective as possible.

Organization and marking of data for accessibility and traceability

AI applications for text mining could allow companies to search their intranet to access customer data and existing information from newly digitized documents. This type of application typically leverages natural language processing (NLP) technology because it can make sense of the text in a document and correlate it to search keywords.

Once integrated into a bank or financial institution’s database, text mining software can provide an accurate search in all types of documents.

Text mining applications could also correlate search keywords with metadata attached to documents in a database. Take the example of a wealth manager who specifically searches for older loan contracts with specific clients. The software could eliminate all documents except loan agreements with these clients within a specified time frame.

Challenges in adopting text mining and metadata markup applications

Text mining applications may require that all documents in a database be tagged with metadata in order to function properly. Business document scanning and search applications will require a business to renovate its data infrastructure to be compatible with the software. That said, it can be a tedious process that companies should consider before going ahead with an AI initiative.

Some machine learning solutions can automatically tag documents with metadata, but the corporate client will still need to define settings for that metadata. The algorithm behind the software correlates meta information over time, such as dates and ID numbers, and could apply it to future research.


Header image credit: Military Times

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