The main goal of this project is to assess both the business impact and ethical considerations of deploying AI in credit scoring. Traditional credit scoring models often rely on limited financial data and static formulas, while AI enables more dynamic, holistic assessments using a broader range of data points such as transaction history, social behavior, and alternative credit indicators. However, with this advancement comes the risk of algorithmic bias, lack of transparency, and data privacy concerns. The project seeks to answer a key question: How can financial institutions leverage AI in credit scoring to improve lending decisions while maintaining fairness, accountability, and customer trust? The outcome will be a business-focused framework that balances innovation with compliance, inclusivity, and ethical governance.
To complete this project, students will first conduct a review of how AI is currently being used in credit scoring and lending decisions across financial institutions. They will compare traditional scoring models (like FICO) with AI-based methods employed by banks and fintechs. Key business benefits such as enhanced credit risk assessment, reduction in default rates, and access to underserved customer segments will be analyzed.
Students will also explore ethical issues, including bias in algorithms, discrimination based on non-financial data, data consent, and transparency in decision-making. Research will include case studies of companies using AI in credit scoring, interviews with financial service professionals, and examination of regulatory frameworks like GDPR, RBI guidelines, and emerging ethical AI standards.
The students will then prepare a comprehensive business report assessing the return on investment (ROI), risk management strategies, and ethical safeguards. They will propose best practices for responsible AI adoption in credit scoring and present a strategic roadmap that aligns AI innovation with customer-centric, fair lending practices.
