Wireframes

In our initial research with stakeholders and data/analytics teams, we learned that SBOs were providing poor or insufficient financial data with their loan applications. So why was the provided data poor and what were SBOs doing wrong?

Did they not understand what they were being asked to provide?

Some first round solutions gave SMOs an aggregated view of their financial landscape by allowing them to connect their financial accounts into a single view. Our solution would then use artificial intelligence to extract relevant data and apply it to loan application documentation for submission.

In addition to connecting bank accounts for data ingestion using Flinks, the team also explored extraction from documentation (uploaded either by file or photo capture) and using transcription data captured directly from SBO voice inputs.

During our interview sessions, we confirmed that SBOs had poor financial literacy and didn’t fully understand what financial models were. They were too focused on other areas of their businesses and relied heavily on the support of friends and advisors to produce the documentation banks required.

One key discovery was that most were very tech savvy and utilized either software or AI to support their business’ accounting needs.

“I regularly use ChatGPT to see how I’m doing… I’m not afraid of giving it my personal information because I know my personal information is already out there, so I might as well use the tools that help me.”—Female, 36

When the interviews had finished and the feedback was analyzed, we concluded that our initial ideas were too complex for a majority of our defined audience and that a lack of financial literacy was the real problem. When we dug into which areas of the financial model were the most problematic, it was determined that cash flow forecasting was the primary culprit of loan rejections.

This outcome highlights the importance of regularly seeking feedback and asking questions. By producing paper prototypes and quickly reviewing them with stakeholders, we were able to produce a solution that best aligned with the problem.

Test 1 – Multivariate

Three variations were created to evaluate each participant’s understanding of the tool’s purpose, the language used (or lack thereof), their interpretation of the data, and overall complexity of the interface

Assumptions:

• Background colours for income/expenses helped people better associate the data within the chart
• Health callout is insightful
• Time frame filter was easily recognized
• Chart displayed right amount of data
• Less is more (minimal labels and support)
• Users understood expense ratio
• A mobile experience was preferred

Results

• Users falsely assumed that the higher the expense ratio number, the better.
• The advisor support question felt inappropriate due to users already using the app and feeling it was important.
• Time frame pill toggles were misinterpreted as being “millions” of dollars.
• Designs were lacking supporting messaging and headlines, such as “cash flow health”.
• Having the advisor callout directly connected to the health was positively received.
• Users want to be able to see individual weekly values by selecting a specific bar on the chart
• The dark highlight of the timeframe filter was missed in design 2 – highlight helps with cognitive understanding of the data
• Not enough information on the chart (weekly data, weekly labels, etc)
• Less tech savvy users want more information to guide them through the experience (labels, headlines, etc)
• Considering changing callout to FAQs, allow users to pick and choose what information they’d like to see
• Consider showing the actual formula so users understand how the health is calculated

“Without labels, I can’t tell what I’m looking at. Sometimes having fewer elements on the screen doesn’t make it simpler, it makes it harder for me to understand.”—Female, 39

Test 2 – A/B Comparison

Two designs were tested to evaluate UI preferences, colours, data, and iconography.

Important note: The data being displayed is merely a snapshot of a singular moment in time and does not take into consideration extraneous income or expenses that may sway the health temporarily. You could have 4-5 weeks of negative cash flow, but in the last couple of weeks you get an influx that sways to the “healthy” state, even though the trend is contrary. Monthly timeframes sway as well, understanding that expenses are typically withdrawn early in the month whereas income typically comes in near the end. So when viewing mid-way through the month, it will look unhealthy until the month is finalized and income balances it out to be healthy.

Results:

Overall, participants preferred option A.

• Simple math equation was easier to understand, whereas the expense ratio was too complex (required users to perform math on their own).
• Green felt positive and encouraging, creating a prompt for action if it wasn’t healthy.
• Drop-down menu was simpler and more intuitive. Users wanted to “set it and forget it” instead of having a quick toggle.
• The larger the chart, the better. Having a call to action for advisor booking only distracts from the key message.
• Design presented improved scannability, avoiding left-to-right eve movement which can increase cognitive load on a screen.
• Chart scale is still confusing. Would be great to have functionality to adjust it.

“I guess an expense ratio makes sense, but do the math for me so I don’t have to. I want to know right away what the result is.”—Female,36