A market trader in Lagos and a banker in London may both be financially responsible.
But an automated lending system could still treat one of them as βriskierβ before either person ever speaks to a human being.
That is one of the hidden problems with AI-driven lending systems.
Many digital loan platforms now use automated models to help decide:
- who gets approved
- who gets rejected
- who receives higher interest rates
- who qualifies for larger loans
These systems are often designed to improve speed, reduce fraud, and predict repayment risk.
But fairness becomes complicated when AI systems are trained on incomplete, biased, or poorly contextualized data.
And in countries like Nigeria, where millions of people work outside highly formal financial systems, this problem becomes even more important.
Not all lenders use the same methods, and not all digital lenders rely heavily on AI. However, alternative data scoring and automated risk analysis have become increasingly common in digital lending.
Here are some ways unfairness can quietly appear.
1. Location Can Become a Proxy for Financial Risk
Some credit models use location-related signals during risk assessment.
The problem is that location can become a proxy variable.
A proxy variable is when a system uses one signal to indirectly estimate something else.
For example, if historical repayment data shows higher default rates in certain areas, a model may begin associating those locations with elevated risk.
This can create problems for responsible individuals living in those same areas.
The system may be statistically optimized, yet still produce unfair outcomes for specific groups of people.
2. Limited Banking History Can Reduce Financial Visibility
Many automated lending systems rely heavily on formal financial records.
People with:
- long banking histories
- regular salary payments
- stable employment records
often appear less risky to predictive models.
But millions of Nigerians earn money differently.
Some people:
- run cash businesses
- work informal jobs
- combine multiple income sources
- use cooperative savings groups instead of traditional banking systems
A person with limited formal banking data is not automatically financially irresponsible.
The issue is that AI systems can struggle when they are designed mainly around highly digitized financial behavior.
3. Device Type Can Influence Predictive Models
Some digital lenders experiment with device metadata as part of alternative credit scoring.
This may include signals related to:
- device age
- operating system
- phone model
- device stability
In some cases, these variables may statistically correlate with repayment patterns or purchasing power.
But predictive correlation is not the same thing as fairness.
A responsible borrower using an older phone could still be financially reliable, while a person using an expensive device may still struggle financially.
This is one reason why technically accurate predictions can still create ethical concerns.
4. Contact Data Raises Serious Ethical Questions
Some lenders have historically collected contact data for purposes such as debt recovery, identity verification, or behavioral analysis.
This has raised concerns around:
- privacy
- consent
- proportionality
- relevance of data collection
Importantly, collecting contact data does not automatically mean it is directly used in machine learning models.
However, large-scale collection of highly personal information can still create ethical and regulatory concerns, especially when users do not fully understand how their data is being used.
5. Behavioral Data Can Be Misinterpreted
Some digital lending systems experiment with behavioral and device metadata as part of risk analysis.
Examples may include:
- app activity
- device usage patterns
- digital engagement behavior
- account stability signals
The challenge is that human behavior is highly contextual.
A student, trader, freelancer, remote worker, or night-shift employee may all use technology differently for perfectly legitimate reasons.
Without sufficient local context, predictive systems may incorrectly interpret normal behavior as elevated risk.
6. Younger Borrowers May Face Structural Disadvantages
Some predictive systems associate younger age groups with higher uncertainty because younger users often have shorter credit histories.
This can make it harder for financially responsible young adults to build credibility within formal lending systems.
The problem is not necessarily direct discrimination.
Often, the system is simply learning historical patterns from existing financial data.
But historical patterns do not always represent fair opportunity.
7. Historical Data Can Reproduce Existing Inequality
Machine learning systems learn from historical data.
If previous lending decisions already reflected unequal access, exclusion, or structural bias, AI systems may unintentionally reproduce those same patterns at scale.
This is one of the most widely discussed concerns in AI fairness research.
Importantly, this does not require malicious intent.
A model can still produce unfair outcomes even when developers believe they are building a neutral system.
8. Existing Borrowers Often Have an Advantage
People with established financial records are generally easier for systems to evaluate.
Meanwhile, first-time borrowers may have very limited digital financial history available.
This can create a cycle where:
- financially visible users become easier to trust
- financially invisible users remain difficult to assess
Over time, this can widen financial inclusion gaps rather than reduce them.
9. AI Systems Can Misunderstand Informal Economies
Many predictive financial systems are built around assumptions common in highly formal economies.
Examples include:
- stable monthly salaries
- fixed residential addresses
- continuous banking activity
- extensive digital financial records
But millions of Nigerians operate within informal economic systems.
This includes:
- market traders
- artisans
- rural entrepreneurs
- freelancers
- small cash-based business owners
Imagine two traders earning similar income levels.
One regularly uses digital banking apps and owns a newer smartphone.
The other mainly operates in cash, shares devices with family members, and has limited digital records.
An automated system trained heavily on digital behavioral data may incorrectly treat the second trader as riskier, even if both individuals are equally capable of repayment.
This is one reason localization matters in AI system design.
10. Digital Behavior Is Not Universal
Behavioral prediction systems rely on patterns.
But patterns are influenced by:
- culture
- infrastructure
- internet access
- electricity stability
- work conditions
- shared technology usage
A system trained mainly on users from one environment may perform poorly when applied to populations living under very different social and economic conditions.
This is not only a technical issue.
It is also a contextual one.
Why This Matters Beyond Technology
Automated lending systems can influence:
- access to credit
- entrepreneurship
- business growth
- financial inclusion
- economic opportunity
Because these systems operate at scale, even small design flaws can affect large numbers of people.
The issue is not that AI is automatically harmful.
The issue is that predictive systems can produce unfair outcomes when they are built without enough understanding of the people they are evaluating.
What Developers and Fintech Teams Should Consider
Better systems are possible.
Some important safeguards include:
Audit training data regularly
Check whether certain communities are being unfairly excluded or misrepresented.
Reduce dependence on weak proxy variables
Not every statistically useful signal should influence financial decisions.
Include local economic realities
Systems designed for African users should reflect African financial behavior, not only Western financial assumptions.
Allow human review and appeals
Users should have meaningful ways to challenge automated decisions.
Improve transparency
People deserve understandable explanations for important financial outcomes.
Test systems across different populations
A model that performs well in one environment may fail badly in another.
My Final Thought On This
One of the biggest risks in AI is not always intentional discrimination.
Sometimes the bigger problem is systems built without enough understanding of the people they affect.
And when those systems influence access to money, opportunity, and financial survival, fairness stops being only a technical discussion.
It becomes a social one.
