Use Automation for 6 Steps of Loan Processing Procedure in '24
The average loan processing procedure takes 52 days to complete.1 The end to end procedure consists of roughly six steps:
- Filling out a loan application
- Processing the application
- Quality control
- Issuing the loan
- Monitoring loan’s performance
Whether from the borrower’s side or from the lender’s, manual completion of these steps can slow down the loans’ processing time, contribute to errors, and needlessly take up everyone’s time.
Banks, credit unions, and other financial lenders can leverage automation to streamline their loan application processing and run a more efficient lending practice.
In this article, we will explain what loan processing (as a whole is), go over the six steps of loan processing, and explain how automation technologies, such as RPA and OCR, can be used at every step of the way to make for a more accurate procedure.
What is loan processing?
Loan processing is a series of steps the lending institution takes to assess a loan application and reach a decision to either extend or reject the loan. The steps include:
1. Loan application
To fill out the loan application, the borrower should provide the lender with their:
- ID information
- Address information
- Income and expenses
- Employment documents
- Credit score
- Bank statements
56% of bankers claim that their biggest challenge when initiating the loan process is manual data collection and the back-and-forth with the applicant. 2
On the part of the applicant, loan applications can be completed online, and supporting papers can be collected from sources and uploaded digitally on the banking portal. For instance, the applicant can submit their recurring bank statement to the loan provider online by downloading it in PDF format.
Or through API, the applicant can share their documents from one application with another instantly via the “Share” button (see Figure 2).
On the lender side, technology such as OCR can read through the document and look for the specifics and extract them. Then, RPA will copy-paste the data onto the bank’s systematized format.
So the initial stage of a loan application, which is document submission, can be made more efficient and streamlined.
2. Loan application processing
Loan application processing is the bank’s employees making sure the documents they’ve received are complete, accurate, and up-to-date.
For instance, a bank statement from three years ago is not relevant to a loan being extended today. Or if the name of the loanee on the application document and the one on their ID does not match, follow-ups should be made to authenticate the applicant.
Manually going over these details – to approve the advancement of the loan application onto the next round – can be time-consuming and error-prone.
Loan processing software leverages IA to:
- Automatically check whether all the required “blanks” have been filled,
- Check whether documents meet pre-approved criteria (i.e., are the bank statements less than one year old at the time of submission? etc.)
- Cross-check the submitted information against sources of truth (i.e., name of the company against their tax slips, etc.)
Another important step in application processing is establishing the credit score of the applicant (see Figure 3). Instead of bank staff manually calling credit agencies to enquire about the credit score of the loanee, the software uses web scrapers, RPA and API to automatically extract credit information from third parties’ websites and put it on the bank’s information sheet.
Loan underwriting is assessing the riskiness of the loan applicant and determining what type of loan they qualify for. A variable that is used to assess the riskiness of the applicant is their credit score. But there are other metrics as well, which can complicate the underwriting process.
ML models can, for instance, predict the possibility of default (PD) of the loanee based on past performances of loan applications that were in a similar:
- Line of work
- Income bracket
- Profit bracket
- Tax bracket
- Credit cohort
Technologies such as RPA, OCR, and NLP further enhance these ML models by understanding the content of the applicant’s submitted documents, adding automated comments next to each field, and comparing their index (i.e. applicant’s level of profit to revenue ratio compared to those in the same field) with industry standards.
The benefit of automating the underwriting process is that automation technologies can, simultaneously and accurately, scan various financial documents – balance sheets, income statements, cash flow statements, etc. – to extract useful information and create data-driven insights from them. An example would be turning YoY (year on year) growth numbers into easy-to-digest charts and graphs.
This speeds up the underwriting process, and by extension, the loan processing procedure. Moreover, based on the performance of each loan application in regard to the rules-based metrics that the bank enforced, the solution itself can make a preliminary decision on the kind of loan that the applicant qualifies for.
4. Quality check
Before processing the loan, the staff performs a final quality check on the borrower’s loan application to ensure everything is in order. Additionally, if at any stage, RPA bots had flagged any piece of information that needed double-checking, the quality check stage is when the latter would happen.
Especially with the increasing number of loan applications annually 4, each with a differing amount and a differing recipient, conducting a thorough revision on each to-be-issued loan can be time-consuming. And it can result in loans being extended to unqualified and unsuited applicants.
The aforementioned technologies such as RPA, OCR, and NLP can act as a final reviewer to go over the applications one last time to ensure the input data in each field meets the rules-based, pre-programmed criteria that the lender and the regulatory agencies expect applications to meet.
5. Loan issuing
The final step is the issuance of the loan and tracking its performance. Once an application has been approved – regardless of the interest rate or the amount the loanee has been cleared for – the decision should be relayed to the customer in a timely manner.
Financial agencies can set up trigger schedulers that would send the loanee a pre-drafted email telling them of their approval/disapproval once the final decision has been made (see Figure 4). This ensures that the customer will receive an answer in a timely manner. And that there are minimal positives or false negatives (i.e., mistakenly telling an applicant they have been approved for a loan when they haven’t or they’ve been approved for a loan with an interest rate below what their credit score warrants).
6. Loan monitoring
And once a loan has been issued, the financial entities should monitor it to make sure the loanee is paying off the installments on time and to measure the likelihood of default based on other loanees in a similar situation (i.e. a cohort of real estate mortgages not being paid on time implies a high-probability of default for others). This gives the bank time to sort out their exposure levels ahead of time and think of possible hedges to secure their position.
Moreover, paid and/or unpaid installments can be tracked and flagged in real-time and be followed up with a consequent email reminding the loanee that their payment had gone through or otherwise. This allows banks to estimate their cash flows accurately and keep track of their bad loans.
For more on FinTech
If you are curious to learn more about how other finance-related processes can be automated, read:
- Leverage These 5 Technologies to Automate Your KYC Process
- 5 Ways Expense Management Automation Can Help Businesses
- Top 4 Benefits of Automating Your Tax Returns
And if you are interested in leveraging a FinTech solution, head over to our FinTech hub where you’ll find data-driven list of vendors.
We will help you choose the best product tailored to your solution:
Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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