Ponco Widagdo, Rida Adela Pratiwi, Herly Nurlinda, Nunung Nurbaeti, Rika Ismiwati, Faisal Roni Kurniawan, Sri Yusriani
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引用次数: 0
摘要
本研究旨在解决投资者和行业参与者使用人工智能(AI)的需求,以更具前瞻性的视角来量化信用风险,而不是传统的非前瞻性方法。本文是对九项研究的文献综述,这些研究涉及人工智能的应用如何提供更好的预测能力,以及需要根据人工智能计算结果做出决策的分析师是否能够充分理解这些结果。我们在 googlescholar 中使用了 "人工智能"、"机器学习 "和 "信用风险 "等关键词。如果没有开放源代码文件,则从 Web of Science 获取全文。共识是相当一致和积极的。人工智能可以提供更好的预测能力,如果使用得当,人工智能可以增加弱势群体获得信贷的机会,这对整体经济有利。然而,要让人们负担得起这项技术,仍存在一些关键挑战,尤其是如何降低复杂性,让更多人学会如何配置、操作和解释人工智能计算结果。本研究希望就人工智能如何帮助更准确地预测前瞻性信用风险量化达成共识。
Artificial Intelligence in Credit Risk: A Literature Review
This study aimed to address the needs of using artificial intelligence (AI) by investors and industry players toquantify credit risk in a more forward-looking view instead of the traditional non-forward-looking methods.This is a literature review of nine studies on how applications of AI used to provide better forecast power, andwhether the results can be adequately understood by analysts who will need to make decisions based on AIcomputation. We use the keywords "artificial intelligence", "machine learning", and "credit risk" in googlescholar. Full text is obtained from Web of Science if unavailable as open-source documents. The consensus isquite consistent and positive. AI can provide better forecast power, and when used correctly, AI can increasethe acceptance for less privileged people to access credit, which is good for the overall economy. However,several key challenges remain to make this technology affordable, especially on how to reduce the complexityso that more people can learn how to configure, operate, and interpret the AI computation results. This studyis looking for consensus of how AI can help more accurate forecasting of forward-looking credit riskquantification.