The Best Loan Origination Software: A Comparison for 2019-2020
Across most industries, software is assuming ever-greater responsibility for making decisions that previously were made by experienced professionals. Algorithms, machine learning, and analytics process volumes of data to enhance process efficiency and decision consistency. These improvements are forcing every industry to evaluate the capabilities of their existing software portfolio.
The lending industry is no exception. Recent advances in lending software (loan origination in particular), should prompt every lender to evaluate their current software’s capabilities.
Loan Origination Software Comparison: The What and the Why
Modern loan origination software offers a wide range of fintech functionality that provides greater implementation flexibility. That lets lenders tailor the lending solution to their specific practices. To help you navigate and evaluate the options, let’s take a look at what’s available and how the new options compare with legacy lending software.
Cloud-Based Loan Origination Software
Cloud-based loan origination software is hosted in data centers by cloud providers like Amazon. Moving from legacy software running on on-premises servers to cloud-based software can reduce CapEx and OpEx. Cloud-based software reduces or eliminates the need to procure and maintain IT infrastructure. It’s quick to implement, since your software vendor automatically provisions the resources. It’s scalable, so as loan volumes change, the software automatically scales up and down to meet the need. Cloud-based software is also high-performance software, since cloud providers are always enhancing their IT infrastructure.
Configuration Creates the Loan Origination Solution You Need
Software configuration eliminates expensive, time-consuming, custom programming needed to transform out-of-the-box lending software into the solution that meets your needs. Configuration menus lending professionals click, drag, drop, and enter their way to modifications in nearly every aspect of the LOS. With configuration you can easily:
- Decide which of the dozens of applicant attributes appear on lending scorecards;
- Specify when and under what conditions automated calls are made to cloud-based lending services;
- Modify decision rules to accommodate changes in credit policies; and
- Tailor the loan application process to the unique needs of your market segments.
With a configuration approach, lending professionals can make modifications by themselves in minutes, compared with the weeks it takes with legacy lending software.
Mobile Support is Imperative
Mobile technologies have transformed consumer expectations of convenient business transactions. Younger users are born digital and live mobile. To remain competitive in the current lending environment, loan origination software must support mobile applications. Look for mobile capabilities that allow you to:
- Create the UI via a WYSIWYG for selecting forms, and arranging fields, images, buttons, and text prompts;
- Apply display logic to guide applicants through the loan application process; and
- Use custom drop-down menus, data masking, rules, and minimum/maximum values, to accurately capture applicant information.
How important is mobile loan application support? In our opinion, failure to do so virtually guarantees a decline in market share.
Automation Drives Efficiency and Decision Quality
Automation has the greatest impact on a lender’s ability to increase efficiency, reduce loan origination costs, and improve decision quality. As the breadth of automated functionality that can be incorporated into lending software continues to widen, lenders should include the following capabilities in their loan origination software comparison for 2019-2020.
Rules drive automation by translating repetitive underwriting decisions—simple, as well as complex—into “if, then, else” commands. Decision rules are easily configured. In a fraction of a second, rules replicate decisions that an underwriter may take minutes to execute. With hundreds to thousands of similar decisions being made daily, rules dramatically accelerate loan origination and bring consistency to lending decisions. Decision rules are also indispensable in helping lenders comply with regulations.
Auto Structuring: Decision Rules on Steroids
Auto structuring extends the concept of decision rules to provide fully-automated loan decisioning. When a loan application fails credit policies, the auto structuring process reviews loan application attributes and iteratively modifies terms with the goal of matching one or more credit policies.
Auto structuring can incrementally adjust interest rates, extend the loan term, stipulate a larger down payment, or recommend a more favorable PTI. When auto structuring arrives at a credit policy match, it initiates an approval or conditional approval. When auto structuring fails to find a match, it initiates an auto decline. Complex decisions like this could take an experienced underwriter fifteen or more minutes. With auto structuring, a decision is reached in seconds. In a competitive lending environment, rapid decisioning increases the likelihood of bookings.
The wealth of consumer data (courtesy of the internet and e-commerce), combined with the willingness of individual consumers to share financial information, can greatly improve lending decision confidence. Cloud-based solutions facilitate easy integration with lending data services as part of the loan origination process.
Alternative Credit Data
Alternative credit data can include real estate ownership, driving records, bank accounts, rental records, and utility payment records: Electricity, gas, water, and mobile phone. Compared with the standard credit data from the Big 3 bureaus, alternative credit data can:
- Complement bureau data to provide a more complete picture of a borrower’s financial position;
- Provide evidence of financial strength for applicants with thin or non-existent credit history; and
- Support more sophisticated loan decisioning to reduce credit risks.
Using decision rules, lenders specify the conditions when alternative credit data information should be accessed during loan origination. Automated calls to alternative credit data sources return information in seconds, and further application of decision rules determines creditworthiness. Alternative credit data give lenders greater confidence in loan decisioning and increases the probability of capturing deals that otherwise would have been declined.
Trended Credit Data
Trended credit data provides up to 30 months of detailed payment history, including balances, minimum payment due, actual payments, and amounts past due. While bureau scores provide a snapshot of financial standing, trended credit data can reveal recent changes in financial behavior.
A subprime applicant who has not missed a payment in the past 18 months, all the while assuming a larger monthly balance, is a better credit risk than bureau score indicates. An applicant with an excellent credit score and 3 late payments during the past year may be facing some financial hardships. Trended credit data gives lenders additional insight into an applicant’s current financial situation to support better quality lending decisions.
Fintech Lending Services Integration
Fintech advancements enabled by machine learning helps lenders make well-informed lending decisions that are unachievable with legacy lending software. Fraud analytics and credit and risk models are two of the most powerful fintech capabilities that help lenders reduce risk without placing additional demands on underwriting staff.
Income and employment misrepresentation of income and identify theft are two of the most frequent fraud schemes associated with loan applications. Machine learning analysis of millions of loan applications, legitimate, as well as fraudulent, identifies obvious and hidden attributes indicating potential fraud. By applying machine learning algorithms to new loan applications at the “point of entry” lenders can eliminate the risk of fraudulent applications that quickly become delinquencies and then defaults.
Credit and Risk Models
Most lenders have hundreds of credit models that have evolved and are constantly updated based on current and anticipated changes in the lending climate. The machine learning approach to credit and risk modeling uses application and portfolio data, bureau tradeline data, and alternative credit data. Analyzing your data, machine learning develops a single credit and risk model that evaluates applicant credentials to approve new loans, assign pricing based on your credit policy, or provide reasons when adverse action notices are required.
Functional Enhancement Options
On occasion, a lender may have a pressing need for software functionality that is not on the software vendor’s roadmap. Recognizing this need, some software vendors work with clients to address on a fix. Functional enhancements are client-funded and typically developed within 30 to 60 days. The opportunity to fund enhancements can be fast track the functions you need for a competitive advantage.
Is It Time for a Loan Origination Software Comparison?
Fintech advancements in mobile applications, data resources, automation, and machine learning give lenders more efficient loan origination processes. If you’re struggling to maintain a competitive position in the current market, outdated lending software may be one of the reasons. A loan origination software comparison of several vendors’ products could be the first step in regaining your competitive advantage.