The potential of leveraging AI in credit scoring is promising.
According to Omotosho (2025):
One of the revolutionary cases that has emerged is the creation of sophisticated artificial intelligence-based credit scoring models, which use innovative machine learning and vast amounts of data on individuals' or companies' credit risks.
However, it isn’t without its challenges:
Systematic discrimination embedded in AI algorithms may lead to unfair outcomes.
In other words, if an AI-based credit scoring tool is trained on data that excluded certain population groups from credit scoring in the past, it will inherit and perpetuate that bias.
To address this issue, Omotosho notes:
The positive correlation between AI adoption and financial inclusion aligns with the study’s expectations and prior research, suggesting that AI can provide more inclusive credit assessments by using alternative data sources.
For instance:
AI-based credit scoring has the potential to go beyond traditional credit data—such as borrower identity, payment history, and credit card usage—to incorporate alternative sources like mobile payment history, utility bill payments, social media activity, and geographical information.