AI-Powered Risk Assessment Models: Transforming Credit Scoring and Default Prediction

AI-Powered Risk Assessment Models: Transforming Credit Scoring and Default Prediction

1Naresh Ogirala,  2 Kandula Nagalaxmi 3  Maddala Paparao   4   Devarakonda Acharao   5 Mundlapati Kishore Kumar

1Naresh Ogirala, Associate Professor, Master of Business Administration, Lakireddy Bali Reddy College of Engineering (Autonomous), Mylavaram, AP-521230. Email: ogiralanaresh179@gmail.com.

2 Assistant professor of dept of MBA, Sree Vahini Institute of Science and Technology (Autonomous), Tiruvuru, AP – 521235.E- Mail: bajjurinagalaxmi@sreevahini.edu.in

3 Professors and HoD of  Department of MBA, Sree Vahini Institute of Science and Technology (Autonomous), Tiruvuru-AP -521235.

  4 Devarakonda Acharao , 23MG1E0025, Student of Sree Vahini Institute of Science and Technology (Autonomous) Tiruvuru. AP -521235.

5Mundlapati Kishore Kumar ,23MG1E0045 Student of Sree Vahini Institute of Science and Technology (Autonomous) Tiruvuru. AP -521235.

Abstract:

This paper examines the transformative impact of Artificial Intelligence (AI) on credit scoring, focusing on AI-driven risk assessment models for credit evaluation and default prediction. The research delves into how machine learning, real-time data processing, and alternative data sources can combine to provide more accurate predictions of an individual's creditworthiness.

The study also addresses critical challenges such as bias, fairness, and the interpretability of AI models, particularly regarding the opaque nature of "black box" AI algorithms. These concerns raise significant questions about transparency and ethical compliance. A primary ethical issue in AI credit scoring is the risk of discrimination, underscoring the importance of robust bias detection and mitigation mechanisms. The paper advocates for transparent and explainable AI models, coupled with strong data governance frameworks. It also emphasizes the need for compliance with legal frameworks such as the General Data Protection Regulation (GDPR) and other relevant laws governing AI applications.

In exploring real-world applications, the paper highlights how AI can promote financial inclusion, enhance risk management and decision-making, and enable faster and more equitable credit evaluations. However, it also addresses challenges such as balancing accuracy with fairness, ensuring data privacy, and improving the interpretability of AI systems.

Emerging trends in the credit scoring industry, such as Explainable AI (XAI), Natural Language Processing (NLP), and blockchain integration, are also discussed as potential drivers of innovation. The study anticipates that regulatory frameworks will need to evolve to address the challenges posed by AI in credit risk assessment. At the same time, it emphasizes the importance of maintaining ethical standards while fostering innovation in this critical area.

Keywords: AI-Driven Credit Scoring, Machine Learning Credit Analysis, Predictive Analytics Creditworthiness, Algorithmic Risk Assessment