Loan origination procedures can be complex and time-consuming. Each step of the way, data is captured, stored, and verified to ensure accuracy— but this doesn’t mean your system has to be a Gordian Knot of laborious manual processes.
There are three main areas of value in Loan Origination Software automation, these being; data capture, underwriting, and funding.
Too many companies still suffer under legacy processes and technologies, pulling that knot tighter and making it harder to keep on top of your admin while your business outgrows them.
Introducing automation to these three key areas cuts through the tedious business of unraveling that knot, getting straight to the point in one swift process.
So what can automation really do for you?
The initial stages of a loan application require a huge amount of data to be obtained, documented, and verified. If done manually, these processes need data entry from employees. This is not only is a waste of time, but also carries the inevitable risk of human error.
Automation helps to not only create an efficient way to capture and store data but also eliminates the most common data entry errors— the kind that could be amplified later in the loan origination process if they arise. For example:
Verify Applicant Address Information
Scenario: An applicant submits an application with an invalid address.
Manual Process: Employee manually identifies the address → Searches the address on a database → Confirms the address is invalid → Finds the correct address → Inputs the correct address into the LOS
Automated Process: LOS is integrated with 3rd party address verification service to authenticate applicant address information, automatically flagging the address as invalid and updates it accordingly
Info From External Systems
Scenario: A loan application is submitted from an external system.
Manual Process: Employee checks over application → Cleans up data where needed
Automated Process: Application data is auto-imported to LOS and data is cleaned
During the underwriting process, a number of standardized business processes will be used time and again, each with its own set of rules.
In the case of a LOS, anything that is standardized can be automated, therefore business rules for underwriting should be configurable and automated.
This goes for mathematical processes such as calculating pricing and debt to enforcing regulations based on origination jurisdiction. It is here, in the underwriting process, where a large part of good LOS’s automation magic can happen. A few examples:
Scenario: An application comes in which falls under an obvious rejection scenario, clearly well below company lending standards.
Manual Process: Employee manually reviews the credit file, exposure data, business rules for exceptions, and plan/program eligibility → The application is rejected
Automated Process: LOS automatically rejects the application based on pre-set lending rules
Scenario: A subprime lender needs to calculate the debt of a potential borrower.
Manual Process: Employee gathers income data from loan application → Calculates total debt → Uses Excel to make the Debt to Income calculation
Automated Process: LOS runs the calculation automatically using information from the application and credit file in real-time
Identifying Pre-Existing Customers
Scenario: A loan application is submitted and where it’s unclear whether the applicant is a pre-existing customer.
Manual Process: Employee copies the name on the application → Looks up the name in the customer database
Automated Process: Each applicant is automatically cross-referenced in the company database and flagged if they are a pre-existing customer
Rules for Financial Products
Scenario: An applicant from Texas requests a small loan. It isn’t clear whether the loan amount and loan type are valid in the state.
Manual Process: Employee identifies the loan amount, loan type, and state of origination → References origination laws for the state → Determines if the proposed loan is valid in the state
Automated Process: Preset rules are set based on the loan origination state, being flagged when the proposed loan does not match the legal standard set for the state
Scenario: The lender uses risk-based pricing mechanisms to determine the rate.
Manual Process: Employee must determine the rate by applying complex rules for base rates, adjusters, and cutbacks on an Excel spreadsheet
Automated Process: LOS automatically calculates the rate, with an audit trail built in to confirm how it arrived at that price
Internal Lending Policies
Scenario: A loan of $20,000 is submitted. A Level 5 employee is handling the origination; however, according to company policy, any loan above $10,000 needs the approval of a Level 10 employee
Manual Process: Level 5 employee emails/texts the Level 10 employee to examine the loan for approval, with application ID in the email.
Automated Process: A Level 10 employee is automatically notified after the Level 5 employee has completed the origination process.
Lastly, the final stages of loan origination call for additional data verification before funding of a loan can be completed. Because this is the final frontier before funds are exchanged, it’s crucial for all information to be completely accurate.
Identity and Personal Information Verification
Scenario: A borrower submits their driver’s license and bank statement to verify their information.
Manual Process: An employee calls the employer to verify employment/income and contacts a landlord to verify residence
Automated Process: LOS is integrated with 3rd party services, which make calls to verify the information → Results are automatically imported into the LOS
Scenario: All necessary documentation for a loan has been compiled and needs signatures from the borrower.
Manual Process: The contract is printed by the lender → The contract is sent to the borrower → The borrower signs the contract → The contract is sent back to the lender
Automated Process: An eSignature is used so the borrower can review the contract electronically and sign at any time → Signature information is immediately sent to the lender
Scenario: A loan is ready to be funded, requiring one last check of the borrower’s information.
Manual Process: Employee calls the borrower to confirm their information
Automated Process: Automated email or text is sent to borrower → Links the borrower to an online portal for confirmation
A Little Time Goes a Long Way
Each of these processes seem small on their own, but together they represent an inconvenient tangle of lost employee time, wasted energy and an increased risk of data entry error.
None of these are practical when you’re planning on business growth.
Thinking about it from a long-term perspective, time savings in particular can be huge. Imagine the time it takes a lender to process 1,000 applications per week when automation can save an average of five minutes per application.
That’s 83 hours per week.
So if you automate the small stuff, 4,316 hours can be saved per year.
Which is more time for employees to cut to the core of the more complex work that automation isn’t equipped to handle.
And granted, easy doesn’t always mean best— but when you can have both, we’d consider that a success.