Artificial Intelligence-Driven Models for Accurate Credit Risk Assessment in Financial Services

• Author(s): Ibidapo Abiodun Ogundeji ; Ejuma Martha Adaga ; Bamidele Michael Omowole ; Godwin Ozoemenam Achumie
• Paper ID: 1707206
• Page: 483-510
• Published Date: 31-01-2022
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 5 Issue 7 January-2022

Abstract

Credit risk assessment is a critical component of financial services, directly influencing lending decisions, regulatory compliance, and overall market stability. Traditional models, often reliant on static financial metrics, face limitations in accurately predicting borrower behavior and managing risk in dynamic economic environments. Artificial Intelligence (AI)-driven models offer transformative capabilities in credit risk assessment by leveraging advanced algorithms, real-time data analysis, and predictive insights to enhance accuracy and efficiency. This paper examines the role of AI-driven models in revolutionizing credit risk assessment practices. Machine learning algorithms, including supervised and unsupervised learning, enable financial institutions to analyze vast datasets, uncover patterns, and develop more precise risk profiles. Natural language processing (NLP) further enhances credit evaluations by extracting insights from unstructured data sources, such as social media and customer feedback. AI-powered systems also support continuous monitoring of borrower risk by incorporating real-time economic, market, and behavioral indicators. The integration of AI-driven models in credit risk assessment delivers significant benefits, including reduced default rates, improved decision-making, and optimized resource allocation. By minimizing human biases and leveraging automated processes, these models enhance efficiency and ensure equitable credit evaluations. Additionally, AI facilitates financial inclusion by enabling credit access for underbanked and underserved populations through alternative data utilization. Challenges such as algorithmic transparency, data privacy, and ethical considerations are addressed, highlighting the importance of robust governance frameworks. Case studies from leading financial institutions demonstrate the successful implementation of AI-driven credit risk models, showcasing improved portfolio performance and risk mitigation. This paper concludes that AI-driven credit risk assessment is a transformative innovation in financial services, enabling institutions to adapt to evolving market conditions while ensuring robust risk management practices. Collaboration among stakeholders, including regulators, financial institutions, and technology providers, is essential to fully realize the potential of AI in credit risk management.

Keywords

Artificial Intelligence, Credit Risk Assessment, Machine Learning, Financial Services, Predictive Modeling, Data Analysis, Financial Inclusion, Natural Language Processing, Risk Management, Algorithmic Transparency

Citations

IRE Journals:
Ibidapo Abiodun Ogundeji , Ejuma Martha Adaga , Bamidele Michael Omowole , Godwin Ozoemenam Achumie "Artificial Intelligence-Driven Models for Accurate Credit Risk Assessment in Financial Services" Iconic Research And Engineering Journals Volume 5 Issue 7 2022 Page 483-510

IEEE:
Ibidapo Abiodun Ogundeji , Ejuma Martha Adaga , Bamidele Michael Omowole , Godwin Ozoemenam Achumie "Artificial Intelligence-Driven Models for Accurate Credit Risk Assessment in Financial Services" Iconic Research And Engineering Journals, 5(7)