Abnormal loan growth can simultaneously alter commercial banks' risk exposure and future credit loss performance. This study uses publicly available quarterly regulatory data to construct an exposure-aligned net write-off ratio for the next four quarters, and examines the potential denominator impact of loan expansion using a growth-neutral net write-off ratio. Using the median loan growth rate of banks of similar size in the same quarter as a reference, the study identifies positive and negative abnormal loan growth, comparing dynamic regulatory risk models, nonlinear generalized additive models, and shallow XGBoost models under strict time sequence. Expanded window prediction and frozen threshold validation show that when the positive abnormal threshold is not triggered, the average error improvement of complex models is limited; after positive abnormal loan growth exceeds 10.8 percentage points, the nonlinear model performs better in terms of average error, tail loss prediction, and high-risk bank identification. The results indicate that commercial banks need to select credit risk measurement tools based on the state of loan expansion.

Minxia Liu. A Comparative Study on Credit Risk Measurement Models of Commercial Banks and Their Applicability. International Journal of Business Management and Economics and Trade (2026), Vol. 7, Issue 3: 23-34. https://doi.org/10.38007/IJBMET.2026.070303.

[1] ENGAN A, HJELKREM LO, RISSTAD M. A review of credit risk assessment in banking—From deterministic models to probabilistic AI[J]. Finance Research Open, 2026, 2(2): 100125. DOI: 10.1016/j.finr.2026.100125.

[2] HEß VL, DAMÁSIO B. Machine learning in banking risk management: Mapping a decade of evolution[J]. International Journal of Information Management Data Insights, 2025, 5(1): 100324. DOI: 10.1016/j.jjimei.2025.100324.

[3] Han, X. (2026). Research on Adaptive Optimization Methods for Multi-Objective Manufacturing Process Parameters in Complex Assembly Processes. Procedia Computer Science, 279, 366-374.

[4] Wang, N. (2026). Research on Integrated Analysis Method of Sales and Operations Data for Management Decision Support.

[5] Ma, X. (2026). Distributed Fault Root Cause Localization and Self-Healing Strategy Generation Based on Causal Inference. Procedia Computer Science, 281, 753-760.

[6] Su, J. (2026). Design and Implementation of Android High Reliability Communication Module Based on Hierarchical Architecture.

[7] Han, X. (2026). Research on Manufacturing Deviation Vehicle Performance Transmission Mechanism and Intelligent Compensation Control for Automotive Engineering. Procedia Computer Science, 281, 594-602.

[8] Wang, Z. (2026). Research on Data Analysis and Quality Prediction of Manufacturing Processes Based on Improved Deep Learning Algorithms. Procedia Computer Science, 281, 1328-1337.

[9] Chi, C. (2026). Quantitative Evaluation of Tranche-Level Returns and Loss Distributions of Structured Credit Assets under Stress Scenarios.

[10] Sun, L. (2026). Resource Scheduling and Elastic Scaling Optimization of Distributed Advertising Systems in Cloud-Native Environments.

[11] Qian, X. (2026). Market Data–Driven Demand Forecasting and Inventory Coordination Mechanisms in Retail Supply Chains.

[12] Yang, Y. (2026). The Integration and Application of Ai Technology in Marketing Data Analysis and Business Decision-Making.

[13] Zhang, Y. (2026). Exploration of Enterprise Big Data Microservice Architecture Based on Domain-Driven Design (DDD). Procedia Computer Science, 282, 1994-2003.

[14] Liu, X. (2026). Construction of Consumer Data Privacy Protection System Based On Blockchain Technology. Procedia Computer Science, 282, 2082-2091.

[15] Liu, X. (2026). Research on the Application of Artificial Intelligence in Consumer Privacy Data Protection. Procedia Computer Science, 279, 956-965.

[16] Zhang, Y. (2026). Research on LLM-Driven Intelligent Architecture Design and Autonomy Mechanism Construction for Cloud-Native Control Planes.

[17] Zhang, Y. (2026). A Framework for LLM-Based Semantic Configuration Understanding and Automated Change Generation in Microservice Orchestration.

[18] Zhang, Z. (2026). Research on Performance Monitoring and Data-driven Optimization Mechanisms for AI Systems Oriented Toward Decision Support. Advances in Computer and Communication, 7(2).

[19] Wang, Z. (2026). Earnings Quality of Resource Enterprises Under Commodity Price Volatility Transmission.

[20] Ma, W. (2026). Reliable Data Infrastructure Supported by Automated Data Pipelines. Engineering Advances, 6(2).

[21] Jin, C. (2026). Research on the Optimization Strategy of Retail Enterprise Product Portfolio Based on Association Rule Analysis. Advances in Computer and Communication, 7(2).

[22] Wang, Z. (2026). Data-driven Pathways for Optimizing Supply Chain Operational Efficiency in Physical Industries. Advances in Computer and Communication, 7(2).

[23] Ma, X. (2026). Research on Elastic Scaling and Resource Scheduling Strategies for Distributed Systems Facing Traffic Fluctuations.

[24] Wang, Z. (2026). Valuation Enhancement Pathways for Resource Stocks Driven by the Growth of Energy Storage Demand. Engineering Advances, 6(3).

[25] Su, J. (2026). Research on Machine Learning-based Performance Prediction and High-reliability Software Evolution Mechanisms for Android Communication Systems.

[26] Zhang, Y. (2026). Autonomous Software Release Management and Cross-service Consistency Control Mechanisms Based on Large Language Models. Advances in Computer and Communication, 7(3).

[27] Chen, J. (2026). Research on Traffic Peak Shaving and Data Consistency Guarantee Mechanism of E-commerce Flash Sale System Under Event-driven Architecture. Engineering Advances, 6(2).

[28] Xin, H. (2026). Research on Distributed Data Cleaning and Standardized Mapping for Heterogeneous Logistics Data. Advances in Computer and Communication, 7(2).

[29] Zhang, Z. (2026). Research on the Optimization of Payment Risk Dynamic Monitoring Models Driven by High-dimensional Behavioral Features. Advances in Computer and Communication, 7(3).

[30] Yuan, Y. (2026). Research on Memory Management and Dynamic Reasoning Methods for Intelligent Agents Based on Large Language Models.