AI-Driven Risk Assessment Models: Revolutionizing Credit Scoring and Default Prediction

• Author(s): Muhammad Ashraf Faheem
• Paper ID: 1702907
• Page: 177-186
• Published Date: 30-09-2021
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 5 Issue 3 September-2021

Abstract

In this paper, we present research into the transformational impact of Artificial Intelligence (AI) on credit scoring and identify AI driven risk assessment models for credit scoring and credit scoring default prediction. This study explores how using the power of combination of machine learning, processing real time data ,alternative data sources can help in predicting more precise an individual’s credit worthiness . In addition to discussing challenges with bias, fairness and interpretability in AI models, especially in black box opaque "AI algorithms" causing concern for transparency and ethical compliance. The first ethical considerations in terms of AI credit scoring are discrimination which requires the strong bias detection and mitigation. The paper also highlights the need for transparent and explainable AI models and data governance, highlighting the enforcement of laws, such as the General Data Protection Regulation (GDPR), amidst other laws, on AI companies. Furthermore the real world applications of AI credit scoring are explored as a means of promoting financial inclusion, enhancing risk management and decision making, and facilitating faster and fairer credit evaluation through AI. On the other hand the study also discusses some of the challenges including interpretability, data privacy, and an accuracy versus fairness trade off. Beyond that, the report also discusses emerging trends such as Explainable AI (XAI), Natural Language Processing (NLP), and blockchain integration as the future possible trend in the credit scoring industry. Further, the study anticipates the regulations to deal with the inevitable challenges in the top of the graph caused by the application of AI in credit risk assessment, without abandoning the ethical basis and encourage innovation in the area of credit risk assessment.

Keywords

AI-driven risk assessment, credit scoring, machine learning, predictive analytics, creditworthiness, algorithmic

Citations

IRE Journals:
Muhammad Ashraf Faheem "AI-Driven Risk Assessment Models: Revolutionizing Credit Scoring and Default Prediction" Iconic Research And Engineering Journals Volume 5 Issue 3 2021 Page 177-186

IEEE:
Muhammad Ashraf Faheem "AI-Driven Risk Assessment Models: Revolutionizing Credit Scoring and Default Prediction" Iconic Research And Engineering Journals, 5(3)