Loan origination automation is critical because the loan origination process is labor-intensive and prone to human error.
The process takes expensive human capital that you can dedicate to other, more strategic tasks. It’s also prone to human error, which increases your costs and puts your reputation at risk.
Automating the process takes humans out of the equation, minimizing the cost of human capital and the risk of human error.
In this article, we explain how to automate the process using an automated loan origination system.
What is the Loan Origination Process?
Loan origination is the process of receiving a mortgage application from a borrower, underwriting the application, and releasing the funds to the borrower or rejecting the application.
When a customer applies for a mortgage, the lender initiates (or originates) the process necessary to determine if a borrower should be lent funds according to the institution’s policies.
The process is extensive and takes an average of 35 to 40 days. The origination process involves five steps, as explained below.
Prequalification is a screening stage. This is where lenders look for potential reasons that can adversely impact a borrower’s capability to repay the loan.
Typically, lenders look for things like:
- Income: Does the borrower make enough money to be able to service the loan payments, and is that income consistent?
- Assets: Should the borrower’s income stop for some reason; do they have enough assets to remain solvent given their existing liabilities?
- Debt: Is the borrower overleveraged? Are the debts secured or unsecured?
- Credit record: Has the buyer made loan payments on time in the past?
These factors help the lender determine if they should spend time processing the application further.
Preparing a Loan Packet
Lenders create a packet (essentially a file of documents) for prequalified borrowers.
The packet includes the borrower’s documents, including KYC, financial statements, and other relevant documents that provide an overview of the borrower’s debt servicing capability.
Lenders also include documents that highlight the reasons that make an applicant eligible or ineligible to be considered for the loan.
For example, the lender may include the borrower’s debt-to-income ratio, properties and assets at market value, and income streams to provide an overview of whether the borrower is a good candidate for the loan.
Some lenders take extra steps to double-check the applicant’s claims.
For example, lenders might hire a valuer or research property rates to verify your real estate investment’s current market value. The valuer’s report is added to the packet for the underwriter’s reference.
Many borrowers, especially those with an excellent credit record, browse their options before accepting a lender’s offer.
The borrower might want to negotiate a lower rate or ask for a fixed rate instead of a floating rate.
Term Sheet Disclosure
A term sheet is a summary of the loan. It’s a non-binding document that contains the terms and conditions of the mortgage deal.
The term sheet includes the tenure, interest rate, principal amount, foreclosure charges, processing fees, and other relevant details.
If the negotiations go well and the borrower accepts the offer, the lender closes the loan.
The lender creates various closing documents, including final closing disclosure, titling documents, and a promissory note.
The borrower and lender sign the documents, and the lender disburses the funds as agreed.
3 Ways to Automate Loan Origination
Now that you know the loan origination process, let’s talk about automating parts of this process to make it more efficient.
Digitizing Loan Applications
You can create an online portal where applicants can initiate a loan application and upload their KYC and other documents.
The documents are automatically transferred to your internal systems for prequalifying the applicant.
IDP extracts and relays the applicant’s data from the documents to your system.
Once the data is in the system, robotic process automation (RPA) can be used to determine if an applicant should be prequalified.
You can use a machine learning (ML) algorithm for deeper insights.
ML can help identify characteristics that make a person more or less likely to service the loan until the end of the term, allowing you to make smarter decisions.
Assembling Loan Documentation
Cloud-based RPA can collect documents from the online portal and organize them in one location. Not only are digital copies faster to collect, but they’re easy to store and search.
Think about it. You’ve received an application, but it’s missing the cash flow statement for last year. You’ll need to email or call the applicant to upload the documents, wasting your and the applicant’s time.
Lenders typically have a document checklist. Automating checklists is easy with RPA — when the applicant forgets to upload a document, the RPA can trigger notifications to the applicant.
The system can also notify the loan officer if the applicant becomes non-responsive.
Speeding Up Underwriting
Borrowers want faster access to funds, but lenders must complete their due diligence.
Lending automation can help reduce the time between application and approval.
With technologies like artificial intelligence (AI), ML, and RPA, automation systems can assess an applicant’s creditworthiness within seconds.
RPA can help with basics like checking the minimum credit score and income levels. RPA can also flag any areas that indicate greater risk, such as excessive variability in the applicant’s cash flows.
Moreover, loan origination is a compliance-heavy process. It’s easy to forget a small step when you’re overwhelmed with loan applications.
RPA ensures all compliance steps are taken care of. If they’re not, the system can trigger alerts for the underwriter as well as the superiors.
AI and ML can provide deeper insights. These technologies can use big data to identify patterns that make a borrower more likely to default, allowing you to make smarter decisions fast.
Moreover, AI and ML can help you look beyond credit scores and find people that are more creditworthy than their credit scores suggest.
As Dimuthu Ratnadiwakara, assistant professor of finance at Louisiana State University, explains:
“Traditional models tend to lock anyone with a low credit score—including many young people, college-educated people, low-income people, Black and Hispanic people and anyone who lives in an area where there are more minorities, renters and foreign-born—out of the credit market.”
How Shortening Loan Origination With Automation Helps
Shortening the loan origination process benefits you in multiple ways:
- Match customer expectations: You’ll deliver on the customers’ expectations by offering faster loan processing. 40% of mortgage customers are willing to complete the entire process using self-serve digital tools, but 67% still interact with a human representative via phone.
- Deliver better experiences: Automation offers various opportunities to improve customer experience. For example, you can set up an AI chatbot that responds to customers’ questions in real time.
- Increase efficiency: You can process more applications per month by automating loan origination.
- Better use of your staff’s time: Automated workflows allow credit officers to focus on parts of the business that require a human touch, such as building stronger relationships with clients, than on repetitive tasks that automation can perform more accurately.
Loan Origination Automation with Blanc Labs
Selecting the right partner to set up loan origination automation is critical. Partnering with Blanc Labs ensures frictionless implementation of a personalized loan origination system.
Blanc Labs tailor-makes solutions best suited for your needs and workflow. Blanc Labs starts by assessing your needs and creating a strategy to streamline your loan origination processes. Then, Blanc Labs creates an automation system using technologies like RPA, AI, and ML.
Book a demo to learn how Blanc Labs can help you automate your loan origination workflow.