A UK-based Nigerian data scientist and researcher, Aminu Lateefat, has advocated the adoption of artificial intelligence-powered (AI) credit scoring tools to help financial institutions reduce loan defaults and improve lending decisions.
She made the call while presenting findings from her research on the application of machine learning models in consumer lending and credit risk assessment.
According to her, the growing demand for loans and increasing complexities in the financial sector require lenders to move beyond traditional credit assessment methods and embrace data-driven technologies capable of accurately predicting the likelihood of loan defaults.
She explained that credit scoring remains a critical tool used by banks and other financial institutions to determine whether loan applicants are likely to repay borrowed funds. However, she noted that conventional statistical models often have limitations in identifying complex patterns in borrower behaviour.
Read More Related News Updates
Man lives for 100 days with artificial titanium heart in successful new trial
Prince William backs AI to help prevent homelessness
Temitope said recent advances in artificial intelligence and machine learning have created opportunities for lenders to strengthen risk assessment processes and reduce exposure to bad loans.
“Machine learning models can analyse large volumes of borrower data, identify hidden patterns and provide more accurate predictions of loan default risks. This enables financial institutions to make better lending decisions and minimise losses arising from non-performing loans,” she said.
The data scientist cited several international studies showing that machine learning techniques such as random forests, artificial neural networks, gradient boosting and support vector machines have delivered stronger predictive performance than many traditional credit scoring methods.
She added that AI-driven credit scoring systems could also improve risk differentiation by helping lenders identify high-risk borrowers more effectively while extending credit opportunities to creditworthy individuals who may be overlooked under conventional assessment models.
Temitope noted that beyond reducing loan defaults, the adoption of AI tools could improve operational efficiency, strengthen portfolio management and support broader financial inclusion.
She, however, warned that financial institutions must address ethical concerns associated with the use of artificial intelligence in lending.
According to her, biased datasets and poorly designed algorithms could result in unfair treatment of certain groups of borrowers, thereby undermining trust in automated lending systems.
She stressed the need for transparency, fairness and accountability in the deployment of AI-based credit scoring models, urging regulators and financial institutions to establish safeguards that protect consumers.
“Individuals should be able to understand how lending decisions are made and have access to mechanisms for challenging decisions they believe are unfair,” she said.
Temitope also advocated stronger data governance practices and enhanced data security measures to protect sensitive financial information used in machine learning models.
She expressed optimism that continued advancements in artificial intelligence would enable the development of more accurate, transparent and inclusive credit scoring systems capable of supporting sustainable growth in the banking sector.
According to her, responsible adoption of AI-powered credit assessment tools could significantly reduce loan defaults while improving access to credit and strengthening financial stability.
For More New Details, Visit New Daily Prime
