Back-to-Back Loans— an evaluative study
Background
FairMoney is a financial service provider that gives access to instant loans which can be applied for on the company's mobile app. The business had realized that a group of users were applying for loans on the same day after the repayment of a previous loan they wanted to understand why.
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Role: UX Researcher
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Duration: 1.5 weeks
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Tools: Google sheets, docs and pivot tables
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Methods: Survey
Objective
The main objective of this study was to understand why these users were making this action. The business needed to understand this to know if they needed to build more robust loan products that gave users access to longer term
Impact
The findings from this research was one of the things that informed the business' decision to provide two new services in the lending ecosystem at FairMoney.
They were- Loan Extensions and Top-up Loans.
The former allowed people to extend the duration of their loans at a cost while the latter gave people access to more loans in the event that they needed more money before the expiration of their loans.
The Process
The Business intelligence team had noticed the aforementioned trend and needed to understand why this was happening.
During the research I worked with the Business Intelligence team, members of the customer service team and the management team.
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This was my first ever research and my process did not follow standard UX Research practices. This is why while it was a survey, it was a phone survey that led to cold calling 500 users and eventually speaking to 201 people.
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The main questions that were asked were:
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What do you do for work?
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What do you use your loans for?
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When you take loans what amount actually satisfies the need you currently have?
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Do you take loans from other lenders?
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Why do you take back-to-back loans?​
The survey calls took place over a period of 4 days. 3 days were spent cleaning and analysing the data and writing the report.
For the actual making of the calls, this is where members of the Customer service team came in. ​​
Findings and Insights
These were the main findings and insights from this research.
For users their business ran on loans and this is why they applied and took loans as frequently as they did​
The interviews revealed significant overlap in B2B loan purposes, with three primary but non-exclusive motivations:
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Business growth (61.7% of clients, including overlapping cases)
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Credit rating improvement (21% exclusively, higher amounts requested)
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Personal needs (frequently combined with business use)
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Notable Data:
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49% of clients had purely business-focused loan needs
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21% sought loans strictly for credit building
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30%+ demonstrated hybrid use cases (business + personal/credit goals)
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Design & Business Implications:
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Product Opportunity: Develop tiered loan products or educational materials addressing these distinct but overlapping user goals.
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Application Flow: Implement dynamic questioning to identify primary use cases early, enabling better personalization.
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Risk Modeling: Flag hybrid-use cases (business + personal) for additional support or verification.
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User Quote:
"I always use my loans to stock my shop, so when I pay back and apply for another one immediately it is because I am trying to buy more things to put in my shop"​
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Users want loan amounts higher than NGN100,000 as this is the amount they believe offers the most value to them
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For the customers who sought back-to-back loans for credit-building purposes they were asked what loan amounts they ae trying to get approved for:
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Notable Patterns:
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Tiered Demand:
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37% preferred micro-loans (<₦100K) for incremental credit building
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20-16% sought mid-range amounts (₦100K-200K)
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Only 18% requested larger loans (₦300K+)
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Behavioral Insight:
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Credit-builders show stronger preference for smaller, manageable amounts
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₦100K appears to be a key psychological threshold
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Recommendations:
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Product Strategy:
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Develop graduated loan tiers aligned with credit-building journeys
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Highlight <₦100K options for first-time credit builders
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Messaging:
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Emphasize "credit-building starter amounts" in marketing
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Educate users on amount selection's impact on credit health
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Risk Management:
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Consider lower-risk approval thresholds for <₦100K credit-building loans
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Monitor repayment patterns by amount tier
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User Quote:
"I started with small loans to prove I could repay, then I noticed that as I paid back on time and applied for more, the loan amount was increasing."​
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Users are stuck in a loop of paying back loans with loans from either FairMoney or competitors
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This is what the competitive landscape looks like:
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62% of surveyed customers use competing loan apps alongside FairMoney:
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Carbon: 13%
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Branch: 11%
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Others: 38%
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38% exclusively use FairMoney.
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Loan Stacking Risk:
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7% admitted taking loans (from family/friends or other companies) specifically to repay FairMoney loans.
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3% were students without income sources, indicating high-risk borrowing behavior.
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Behavioral Insights:
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Multi-app dependency is common, with Carbon and Branch as top competitors.
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A small but critical segment (7%) engages in high-risk loan cycling (using new loans to repay existing ones).
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Students represent a vulnerable group needing safeguards.
Recommendations:
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Risk Mitigation:
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Flag repeat borrowers who show patterns of loan stacking (e.g., frequent loans near repayment dates).
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Implement stricter affordability checks for high-risk groups (e.g., students without income).
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Competitive Analysis:
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Investigate why customers use Carbon/Branch (e.g., better rates, higher limits?) to address gaps.
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Customer Support:
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Proactively offer repayment plans to at-risk users (e.g., the 7% loan cyclers).
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Develop educational content on debt management for vulnerable segments.
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Product Design:
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Explore "loan consolidation" features for users juggling multiple apps.
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User Quote:
“I used Branch when my FairMoney limit was exhausted, but the interest was higher.”​​​​​​
Take-Aways
As previously stated, this was my first UX Research project here are things I would do better:
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Better planning: This research did not have a defined objective and it led to not using the best research method for this research. This also led to inefficient data collection. ​
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More Efficient Analysis Methods: While I used Pivot tables to analyse the data gotten from this research, I was at the same time learning Google sheets and Pivot table, this led to errors that extended the analysis period for the research.