In today’s rapidly evolving financial landscape, technology is playing a pivotal role in promoting financial inclusion and economic empowerment.
In a recent interview on how technology is reshaping financial realities for economic inclusion, Financial risk management expert, Adeyinka Elizabeth Orelaja highlights the need to utilize technology for sustained financial empowerment and inclusion through improved credit scores for individual’s prosperity.
“Traditional credit scoring models have long been faulted for their inability to accurately assess the creditworthiness of individuals with limited or non-traditional financial histories, encouraging a cycle of exclusion for these groups of people. However, with predictive modeling enhanced by artificial intelligence (AI), machine learning (ML), and alternative data sources such as rent receipts, utility bills even social media activity, we can revolutionize the way credit scores are calculated, opening up new avenues for financial inclusion and economic empowerment such that no one is excluded from financial growth opportunities because of their lack of traditional credit history” Adeyinka explained.
“Credit scores are not just a financial scorecard to ascertain if you deserve to obtain the financial help you need or not. It goes beyond that. They are powerful tools that if harnessed have the potential to reshape economic realities for a lot of people, transform communities and drive economic growth within the country” she emphasized.
In her work on “Promoting Financial Inclusion and Economic Empowerment: Enhancing Credit Score Classification with Random Forest to Bolster the US Economy and Support Worker Prosperity”, Adeyinka explored 10,000 records of a comprehensive dataset with 28 variables focusing on credit scores classified into poor, good and standard categories. In this exhaustive analysis, Adeyinka highlights profound insights into the correlation between credit scores and predictor variables that affect economic outcomes of workers such as banking habits, income levels, loan histories, interest rates, payment behaviors and credit card use. Through a meticulous training and testing framework, a random forest model to evaluate credit score classifications was employed, showcasing an accuracy rate of 81.29%. The results of the research underscores outstanding debt, credit mix, interest rates, credit history duration, payment delays, and alterations to credit limit as the most significant variables influencing creditworthiness.
By harnessing the potential of predictive analysis, Adeyinka’s work not only sheds light on the complexities of credit scoring mechanisms but also serves as a trigger for informed decision-making among policymakers, financial institutions, and society generally. With a rich background leveraging advanced analytics technology to drive innovative solutions, Adeyinka has distinguished herself as a leader in the finance industry. Her efforts laud the overarching potential of technology to transform economic growth and foster inclusivity in financial systems and are a significant step towards achieving financial inclusion and economic empowerment, ensuring that more individuals have access to the resources they need to thrive despite changing economic realities.
