Developing AI-Powered Credit Scoring Models Leveraging Alternative Data for Financially Underserved US Small Businesses

In the modern dynamically changing financial landscape where people more and more often are illegible with traditional credit scoring systems, traditional credit scoring systems are in many cases ineffective to capture the credit worthiness of the underserved small business borrowers- especially those who do not have lengthy credit histories or traditional documentation. In their study, the authors focus on how to develop and implement the artificial intelligence (AI)-based credit scoring models, which can solve these shortcomings by utilizing alternative sources of data. The objective is to improve the accuracy, diversity and equity of lending decisions to marginalized business owners of small firms that are usually underrepresented in regular financial services. Using a large scale data containing demographic, financial, employment, and behavioral characteristics, this study will utilize enhanced machine learning technologies with the help of gradient boosting and decision tree methods to assess borrower risk of default based on a multidimensional applicative nature. The parameters namely education level, marital status, bracket of income and job tenure are examined to reveal how they impact their viability of loan default. Those findings display specific trends: the more highly educated individuals the longer their employment term, and the stable marriage status, the less likely they are to default, which proves the predictive qualities of using non-traditional factors to evaluate credit risks. The results of this study add to a more inclusive credit assessment system because they indicate that the alternative data points can be used to reliably predict credit worthiness. This study highlights the possibility of AI underpinning data-driven, fair lending platforms and decreasing financial exclusion. In addition, it promotes responsible AI, which entails that algorithmic decisions are explainable, ethical and do not produce systemic bias. This study would contribute to the discussion of financial inclusion by presenting a solution that gives leeway to financial institutions, financial innovators, and policymakers. Extending credit to small businesses increases revenue by providing lenders with different data options informed by AI-driven models to recognize deserving credit applicants that may otherwise have been turned away. This increases the flow of capital not only, but enhances growth at the grassroots level- which works in line with wider agendas of sustainable and inclusive financial growth.