Automated Loan Decisioning Explained

The ways legacy banks have done loans for decades is over. Now, automated loan decisioning is an essential form of BaaS for the growth of a lending startup, as it enables operational tasks to be automated. The primary solution for this is the use of automated loan decisioning.

Automated loan decisioning involves the instant evaluation of loan applications, enabling applicants to accept offers seamlessly. This automation accelerates the disbursal process, increases loan volume, and enhances customer satisfaction.

To gain a better understanding of the benefits of automating loan origination, this post will examine the loan decisioning process and its advantages for end-users.

4 Steps of Automated Decisioning

The automated decisioning process comprises four essential steps, which are as follows:

1. Collect customer-provided data
2. Gather inferred data from third-party sources
3. Acquire inferred data from behavior
4. Analyze and process the data using the decision engine

All of these steps are crucial in the automated decisioning process, and we will delve into each step in more detail below.

Step 1: Collect Customer Data

The initial step in automated decisioning involves the collection of all customer-provided data. While the specifics of the data requested may vary across lenders, some universal pieces of information include:

• Name
• Income
• Social Security number
• Requested loan amount

Traditionally, loan and credit applications were in the form of paper documents. However, this process was laborious and time-consuming, with a high risk of human error during manual review and data input.

Today, digital and online forms have become ubiquitous, allowing lending startups and other organizations to collect and process customer-provided data within minutes, if not seconds. This approach offers several benefits, such as identifying missing information and enabling software to contact end-users to complete any missing fields or request corrections for improperly filled forms.

Step 2: Collected Third-party Data

The following step in the automated decisioning process involves the software API extracting “inferred” data from third-party sources. Third-party inferred data refers to data obtained from sources other than the end-users that can assist in the decision-making process. Examples of such data include, but are not limited to:

• Bank and accounting records
• Tax returns
• Identity verification
• Business and personal credit scores
• Industry information

Each of these pieces of data provides vital, up-to-date information about whether the potential borrower is a suitable fit for the company’s risk model. Moreover, the software API can accomplish this task within minutes, saving time and effort for both the applicant and the lender.

Step 3: Collect Behavioral Data

The third step in an automated loan origination platform is to gather additional information that the potential borrower has authorized, or customer behavioral data. This step may include collecting data from social media and other online purchasing behavior, among other types of data.

For instance, a potential borrower might post pictures of expensive purchases, such as a luxury car or a vacation, on Twitter or Facebook. Analyzing this data through the AI decisioning engine could suggest that the potential borrower has poor financial responsibility.

Alternatively, behavioral data analysis may help recommend additional or alternative loan products for a potential borrower. Social media posts about a luxury car could indicate that the potential borrower may require an auto loan, while posts about a vacation may suggest that a personal loan could be an appropriate product for the borrower.

Step 4: Interpret Data Via Decision Engine

After gathering customer-provided data and the two types of inferred data, they can be merged and analyzed through the AI decision engine. The AI can instantly evaluate the data and make decisions about loan applications based on the information.

Automated loan origination is not intended to replace a company’s lending process. Instead, its purpose is to expedite and streamline the process. The most effective solutions are developed based on a set of criteria established by the lender, which informs the system about how and when to approve an application.

If the Application Meets the Parameters

If a loan application meets the established criteria, the system can forward it to the underwriting stage for further evaluation. If your credit or lending product utilizes the Onbo API, this step can also be automated. The underwriter will examine the borrower’s available collateral, financial ratios, and other indicators of creditworthiness. If the application is approved by the underwriter, it can proceed to the AI decisioning engine.

The decision engine uses the company’s criteria to assess the risk, and based on the output of this calculation, the system will adjust the loan terms accordingly. After that, the system (or a loan officer) can offer the loan to the borrower.

If the Application Doe Not Meet the Parameters

Even if an application falls short of the minimum criteria, automated decisioning still enhances the customer experience. When a loan application is denied, the automated system can activate any number of predetermined actions, such as informing the borrower that their application cannot be approved at present or directing the applicant to a referral partner. Since all of this is automated, it happens promptly, giving the customer a sense that their time was not squandered.

3 Ways Automated Decisioning Improves UX

In the preceding sections, we explored some of the customer experience advantages of automated decisioning. In this section, we will delve into the most significant benefits that your end-users will derive. These crucial benefits include:

1. Flexible application processes
2. Immediate answers
3. Expedited disbursals

Now, let’s examine each of these essential advantages in greater depth.

1.  Flexible Application Processes

In the past, borrowers had to visit a lender or bank during business hours to obtain a paper application. Nowadays, they can complete a loan application online in just a few minutes from any personal device, anytime and anywhere. This is the result of an omnichannel, online application process. Consequently, this approach extends your company’s business hours around the clock and grants borrowers the flexibility to apply at any time.

2. Instant Answers

A potential borrower’s primary concern is whether their loan application will be approved or declined. Real-time decisioning makes this possible and instantaneous.

Automating your decision-making process reduces loan processing time. Previously, applicants had to wait weeks before learning that their application had been rejected. Automation compresses this process into a few days, with an average of 3-7 days, although Stilt can accomplish it in as little as 24 hours. When a decision is reached, the system immediately sends out the result.

This produces a high level of satisfaction among potential borrowers, even those who are not approved. Applicants value not having to wait or being given the run-around and may reapply when their situation improves.

3. Quicker Disbursals

Ultimately, borrowers desire to receive their loan funds in their bank accounts as rapidly as feasible. Automating your workflow expedites the disbursal process. When your operations team is not swamped with paperwork and documents to review, they have more time to concentrate on customers and offer value-added services.

Furthermore, automating your loan processing results in a greater number of loan applications being processed more quickly. As a result, loan disbursal speed and customer satisfaction increase, and disbursal volume rises.

Final Thoughts

By this point, the advantages of automated loan decisioning should be self-evident. Establishing and executing an AI-based decisioning engine not only produces a more enjoyable and satisfactory experience for your customers, but it also offers an opportunity to expand your business. Processing more loan volume more rapidly than would be feasible with a manual or semi-automated system – implementing automated loan decisioning seems like an obvious choice.

Frank Gogol

A seasoned SEO expert, Frank has a long history of working with and for startups. Starting in mid-2018, Frank served as the SEO Strategist for Stilt, a fintech startup that provided fair loans for immigrants in the US and other underserved markets. While with the company, he scaled site traffic from zero to more than 1.5 million unique visits per month, driving the bulk of the company’s lead generation until it was acquired by J.G. Wentworth in December 2022. As employee #5 at Stilt, Frank was witness to, and part of, the successful building and sale of a fintech company, uniquely positioning him to create content for founders about all things startups.

See author's posts