Artificial Intelligence Applications in Credit Risk Assessment

Cristián Bravo Roman

Artificial Intelligence Applications in Credit Risk Assessment

Author(s)	Cristián Bravo Roman
Country	United States
Abstract	The rapid advancement of Artificial Intelligence (AI) technologies has significantly transformed financial decision-making processes, particularly in the area of credit risk assessment. Traditional credit evaluation approaches often depend on historical financial records, limited customer information, and conventional statistical models, which may create challenges in accurately assessing complex borrower behaviours. AI-driven approaches provide advanced analytical capabilities through machine learning algorithms, predictive modelling, and automated decision systems that enhance the efficiency and accuracy of credit risk evaluation. This study examines the applications of Artificial Intelligence in credit risk assessment by exploring how AI technologies improve risk prediction, borrower evaluation, fraud detection, and lending decision processes. The research evaluates the role of machine learning models, alternative data analysis, automated scoring systems, and predictive analytics in enhancing credit assessment performance. A conceptual analytical framework supported by simulated observations is developed to examine the contribution of AI-based approaches toward improved credit risk management. The findings indicate that AI applications can significantly improve credit assessment accuracy, reduce processing time, enhance fraud detection capabilities, and support more inclusive lending decisions. However, challenges related to data privacy, algorithmic bias, model transparency, cybersecurity, and regulatory compliance continue to influence responsible AI adoption in financial services. The study contributes to financial technology and risk management literature by explaining the strategic importance of AI in modern credit evaluation systems. The findings provide practical insights for banks, financial institutions, regulators, and technology providers seeking to implement intelligent and responsible credit risk assessment frameworks.
Keywords	Artificial Intelligence, Credit Risk Assessment, Machine Learning, Financial Technology, Predictive Analytics, Automated Lending, Risk Management.
Field	Engineering
Published In	Volume 2, Issue 6, November-December 2020
Published On	2020-11-10

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American International Journal of Research and Innovation

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Artificial Intelligence Applications in Credit Risk Assessment

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