With the rapid growth of technology, especially the widespread application of
artificial intelligence (AI) technology, the risk management level of
commercial banks is constantly reaching new heights. In the current wave of
digitalization, AI has become a key driving force for the strategic
transformation of financial institutions, especially the banking industry. For
commercial banks, the stability and safety of asset quality are crucial, which
directly relates to the long-term stable growth of the bank. Among them, credit
risk management is particularly core because it involves the flow of a large
amount of funds and the accuracy of credit decisions. Therefore, establishing a
scientific and effective credit risk decision-making mechanism is of great
strategic significance for commercial banks. In this context, the innovative
application of AI technology has brought revolutionary changes to bank credit
risk management. Through deep learning and big data analysis, AI can accurately
evaluate the credit status of borrowers, timely identify potential risks, and
provide banks with more accurate and comprehensive credit decision support. At
the same time, AI can also achieve realtime monitoring and early warning,
helping banks intervene before risks occur and reduce losses.

AI 技术在商业银行的信用风险管理中的革命性应用对建立科学有效的信用风险决策机制具有重要战略意义，通过深度学习和大数据分析，AI 能够准确评估借款人的信用状况、及时识别潜在风险，并为银行提供更准确全面的信用决策支持，同时实现实时监控和预警，帮助银行在风险发生之前进行干预，降低损失。

银行信用风险管理中人工智能技术的创新应用

Innovative Application of Artificial Intelligence Technology in Bank  Credit Risk Management

The discussion on causality in human history dates back to ancient Greece,
yet to this day, there is still no consensus. Fundamentally, this stems from
the nature of human cognition, as understanding causality requires abstract
tools to transcend the limitations of human cognition. In recent decades, the
rapid development of mathematical and computational tools has provided new
theoretical and technical means for exploring causality, creating more avenues
for investigation.
Based on this, this paper introduces a new definition of causality using
category theory, proposed by Samuel Eilenberg and Saunders Mac Lane in 1945 to
avoid the self-referential contradictions in set theory, notably the Russell
paradox. Within this framework, the feasibility of indicator synthesis in
causal inference is demonstrated. Due to the limitations in the development of
category theory-related technical tools, this paper adopts the widely-used
probabilistic causal graph tool proposed by Judea Pearl in 1995 to study the
application of causal inference in personal credit risk management. The
specific work includes: research on the construction method of causal inference
index system, definition of causality and feasibility proof of indicator
synthesis causal inference within this framework, application methods of causal
graph model and intervention alternative criteria in personal credit risk
management, and so on.

本文介绍了一种基于范畴论的新的因果关系定义，并在此框架下论证了指标综合因果推断的可行性，同时还探讨了因果图模型和干预替代标准在个人信用风险管理中的应用方法。