Theoretical frameworks in AI for credit risk assessment: Towards banking efficiency and accuracy
1 Independent Researcher, London Ontario, Canada.
2 Independent Researcher, Hamilton, Ontario, Canada.
Review
International Journal of Scientific Research Updates, 2024, 07(01), 092–102.
Article DOI: 10.53430/ijsru.2024.7.1.0030
Publication history:
Received on 10 February 2024; revised on 17 March 2024; accepted on 20 March 2024
Abstract:
This paper delves into theoretical frameworks in AI for credit risk assessment, exploring how these frameworks enhance banking efficiency and accuracy. It discusses various AI techniques such as machine learning algorithms, neural networks, and natural language processing, and their application in credit risk assessment. Furthermore, it examines the challenges and opportunities presented by these frameworks, highlighting their potential to revolutionize the banking sector. Revolutionizing Credit Risk Assessment in Banking, The Role of Artificial Intelligence In the dynamic realm of finance, the assessment of credit risk stands as a fundamental pillar for banking institutions. Traditionally, this process has heavily relied on statistical models and historical data. However, the emergence of Artificial Intelligence (AI) has catalyzed a transformative shift in this domain. This paper elucidates the theoretical underpinnings of AI frameworks employed in credit risk assessment and investigates their profound implications for enhancing the efficiency and accuracy of banking operations. The exploration begins by delineating various theoretical frameworks in AI pertinent to credit risk assessment. Leveraging machine learning algorithms, neural networks, and natural language processing techniques, these frameworks offer innovative approaches to evaluate creditworthiness. Unlike conventional methods, AI-driven models possess the capacity to ingest vast datasets, identify intricate patterns, and adapt dynamically to evolving market dynamics. Such capabilities empower banks to make more informed and timely decisions regarding lending activities. Moreover, this paper delves into the practical application of AI techniques in credit risk assessment. Through case studies and empirical evidence, it elucidates how these advanced methodologies enable banks to mitigate risks while maximizing profitability. By harnessing AI, financial institutions can optimize credit scoring processes, identify potential defaulters with greater accuracy, and customize lending terms based on individual risk profiles. Additionally, AI facilitates real-time monitoring of credit portfolios, allowing proactive risk management and timely interventions to prevent adverse outcomes.
Keywords:
Artificial Intelligence (AI); Credit Risk Assessment; Banking Efficiency; Banking Accuracy; Machine Learning; Supervised Learning
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0