In today’s rapidly evolving financial landscape, I believe technology plays a pivotal role in promoting financial inclusion and economic empowerment.
In a recent interview where I discussed how technology is reshaping financial realities to support economic inclusion, I emphasized the urgent need to leverage technology for sustained financial empowerment—particularly by improving credit scores as a pathway to individual prosperity.
Traditional credit scoring models have long been criticized for their failure to accurately assess the creditworthiness of individuals with limited or non-traditional financial histories. This often perpetuates a cycle of exclusion. However, with the help of predictive modeling powered by artificial intelligence (AI), machine learning (ML), and alternative data sources such as rent payments, utility bills, and even social media activity, we can transform the way credit scores are calculated. These new approaches open doors to financial inclusion and economic opportunity, ensuring that individuals are no longer sidelined due to the absence of a traditional credit history.
Credit scores are not merely financial scorecards used to determine eligibility for financial assistance—they are powerful instruments. When properly harnessed, they have the potential to transform individual economic trajectories, revitalize entire communities, and stimulate national economic growth.
In my research titled “Promoting Financial Inclusion and Economic Empowerment: Enhancing Credit Score Classification with Random Forest to Bolster the US Economy and Support Worker Prosperity,” I explored a dataset of 10,000 records with 28 variables focusing on classifying credit scores into poor, good, and standard categories. Through this comprehensive analysis, I uncovered important correlations between credit scores and predictor variables that shape workers’ economic outcomes—such as banking habits, income levels, loan histories, interest rates, payment behaviors, and credit card use. By applying a rigorous training and testing framework using a random forest model, I achieved a credit score classification accuracy rate of 81.29%. The analysis revealed that outstanding debt, credit mix, interest rates, length of credit history, payment delays, and changes to credit limits are among the most influential variables affecting creditworthiness.
Through this work, I aim to demystify the credit scoring process and use predictive analytics as a catalyst for informed decision-making—whether by policymakers, financial institutions, or society at large. With my experience in applying advanced analytics to drive innovative solutions, I am committed to using technology to create inclusive financial systems. My efforts are part of a broader vision to ensure that more individuals can access the financial resources they need to thrive, regardless of the traditional barriers they may face in today’s changing economic environment.