Innovative Application of Artificial Intelligence Technology in Bank Credit Risk Management

Paper: arXiv 2404.18183

Authors: Shuochen Bi, Wenqing Bao

Abstract

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.

Complexity vs Empirical Score

• Math Complexity: 1.5/10
• Empirical Rigor: 2.0/10
• Quadrant: Philosophers — conceptual discussion, limited math and data

Why this score: The paper provides a very high-level, descriptive overview of AI in bank credit risk management without introducing mathematical models, formal algorithms, or empirical backtesting. It lacks original technical methodologies, empirical datasets, or practical implementation details necessary for replication.

Research Flowchart

  flowchart TD
    A[Goal: Enhance Bank Credit Risk Management via AI] --> B[Data Inputs: Big Data & Borrower Profiles]
    B --> C[Methodology: AI, Deep Learning & Big Data Analytics]
    C --> D[Processes: Risk Scoring, Real-Time Monitoring & Early Warning]
    D --> E[Outcomes: Precise Credit Decisions & Proactive Loss Reduction]