Loan Approval Guide
I spearheaded the design of a new feature aimed at helping users understand why they were declined for a loan. Through our project, we confirmed our hypothesis, resulting in a notable 17% engagement.
Year
2022
Duration
3 Months
Role
Product Designer
Team
Product Manager,
Engineer Lead,
Business Analytics,
User Researcher
The problem
50% of users who apply for a personal loan at Credible are declined. That's roughly 80,000 declines per month! When users are declined they see the decline screen below:
Why so many declines?
My product manager and I met with the PM for Personal Loans and learned the two main reasons for declines:
Low credit score. An applicant’s credit score is one of the most important factors a lender considers when evaluating a loan application.
High debt-to-income. Lenders use this to predict a prospective borrower’s ability to make payments on new and current debt.
71% rated the page as “not at all helpful”
I launched an in-product survey to understand how users felt after getting declined. We received over 400 responses! The data we collected from this survey was invaluable. It helped us understand how users felt about getting declined.
Unfortunately, an overwhelming 71% of users found this page unhelpful. It's evident that this was a poor experience for our users.
Why so unhelpful?
My hypothesis on why users rated it so poorly was that our current decline screen didn't provide any information besides “You're declined.” The page didn't offer much assistance or clarify the reasons for the decline.
Helping users get approved
My PM wrote our opportunity statement: “We believe creating a coaching experience for declined users can improve their financial well-being and lead the user back to Credible.” In short, our goal was to help declined users get approved for a loan.
In order to achieve our goal, I presented a proposal to my PM aimed at creating a new feature to solve this problem. I suggested creating a minimum viable product to gauge user interest and validate the potential value of this idea.
Research insights
Following the completion of the decline survey and thorough review of all comments, I delved into our support calls as well. I culled through all the feedback and saw three signification insights:
Users want to know why they are declined. My initial hypothesis was correct –users wanted to why they were rejected for a loan. Looking at the support call data I saw this trend persist month after month.
Users have a poor understanding of approval factors. Users often boasted about their high income while neglecting other approval criteria. They fixated solely on one aspect of approval, overlooking the multitude of factors that contribute to getting approved.
Users want to see the minimum credit score. Users want to see the minimum credit score. People were searching for a north star. In the survey feedback, a recurring question emerged: "How close was I to the minimum credit score?"
How do we solve this?
One of the things we knew earlier on was we probably weren't going to completely solve this problem in our first phase. After lots of working sessions, I got alignment with my PM and CPO on our goals:
- Tell user the minimum credit score to get approved
- Educate user on approval factors
- Provide generic decline reasons
First exploration
In my first exploration, I wanted to highlight to the user the difference between their current credit score and the minimum score required for approval. My goal was to help users understand the extent to which they needed to increase their score.
Second exploration
One of our goals was to educate the user on approval factors so my next explorations focused on educating the user about the approval process but it was extremely text-heavy.
Third exploration
Our lead engineer, Diego, suggested we show an average of minimum credit scores. I thought this was a great idea. We discussed with our CPO about our early legal pushback and he told us to move forward.
“This isn’t novel or creative.”
After many iterations with my design peers and PM, I shared a final concept with the CPO during a design review. He didn’t think the solution was strong enough and told me to iterate more.
Back to the drawing board
I realized that my designs were lacking in inventiveness and delight. I shifted my approach, stepping away from simply expecting users to read for value, and instead opted for a more interactive experience. I presented the page in the format of a quiz.
To help users get approved I believe we first needed to help them understand why they were declined in the first place. I shared the new concept with my design peers and they resonated with the new direction it was heading in.
Each section would focus on an approval factor. This section focuses on minimum credit score, one of the most important factors.
We didn't have a subject matter expert to assist so I read countless articles on what lenders are looking for and I synthesized the most important factors.
User testing before launch
Before the launch, I worked closely with our researcher to come up with a test plan. I drafted an initial set of questions and she helped me refine them. I then proceeded to conduct tests on both the existing concept and the new interactive one I had developed.
I launched an unmoderated user test with ten users.
“I think this should be shown before you even apply.”
The user test results:
Users rated the content as “very useful.” One user said, “...I think [the loan approval guide] should be shown before you even apply...”
Users find debt-to-income calculator extremely useful. Users said that this was their first time hearing about DTI.
Users do not find "proof of income" and "stable employment history" useful. However, this is important for users to know.
We launched!
We launched the MVP to a subset of our users in June 2022. Here are some key metrics post-launch:
17% of users clicked into the guide
Early in the design process, I made a decision not to remove the current decline experience. My rationale was to ensure clarity in validating the MVP by testing the new feature with a CTA. This approach allowed us to gauge user interest by measuring deliberate clicks, thereby confirming whether users desired this feature.
This loan approval guide was getting 250-300 daily visits and 17% of users were visiting the guide!
Over 50% of users on the page are engaging with each content block.
Users were clicking and reading the content. Naturally, we saw credit score with the most engagement, likely due to position, (58%), followed by debt-to-income (55%).
Users found the content helpful!
Yes, ~117% more helpful thumbs up than thumbs down. This was a huge improvement over the original decline experience. This was a late addition and I was surprised that users were even clicking on this.
Final thoughts
So how did the Loan Approval Guide do? Did my project prioritization show that this was a strategically correct decision? Yes!
This was a huge success for Credible and for our users. We focused on a screen to help them understand why they were declined. From the data, we saw that we need to double down on this experience.
We knew that helping users get approved wouldn’t happen overnight so we needed two types of metrics. The short-term metric would help us validate if users would want this feature, remember, that this was a new feature and a huge bet. We still didn’t know if users would even engage with this. The long-term metric would help us understand if we achieved the outcome we wanted.