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

In this paper we present a novel approach to credit scoring of retail
customers in the banking industry based on deep learning methods. We used RNNs
on fine grained transnational data to compute credit scores for the loan
applicants. We demonstrate that our approach significantly outperforms the
baselines based on the customer data of a large European bank. We also
conducted a pilot study on loan applicants of the bank, and the study produced
significant financial gains for the organization. In addition, our method has
several other advantages described in the paper that are very significant for
the bank.

本文提出了一种基于深度学习方法的银行零售客户信用评分的新方法。我们使用 RNNs 分析精细的跨国数据来为贷款申请人计算信用评分，并证明我们的方法显著优于基于某大型欧洲银行客户数据的基线方法。此外，我们在银行的贷款申请人上开展 pilot study，该研究为该机构带来了显著的财务收益。本文还描述了我们的方法具有的其他一些非常重要的优势。