AI-Powered Credit Risk Assessment: Transforming Lending in FinTech (Published)
This comprehensive article examines the transformative impact of artificial intelligence in FinTech lending, particularly for underserved populations. Traditional credit scoring systems have proven inadequate for evaluating “credit invisible” individuals who lack conventional financial histories but demonstrate genuine creditworthiness. The integration of AI and machine learning models, including random forests, gradient boosting machines, and neural networks, enables lenders to incorporate alternative data sources like digital transactions, mobile usage patterns, and behavioral indicators. Through case studies like MicroCredit Fintech, the research demonstrates how AI-powered risk assessment can significantly reduce default rates while expanding credit access. Implementation requires phased deployment methodologies, robust privacy frameworks, and organizational change management. The resulting benefits include improved loan approval accuracy, reduced operational costs, and enhanced financial inclusion. This transformation represents not merely a technological shift but a fundamental reimagining of credit assessment that balances innovation with ethical considerations and regulatory compliance.
Keywords: Financial Inclusion, alternative data analytics, credit risk modeling, lending optimization., machine learning implementation