International Journal of Social Sciences and Economic Management, 2026, 7(1); doi: 10.3807/IJSSEM.2026.070111.
Li Fang
Tianjin University, Tianjin 300072, China
Against the backdrop of rapid development of consumer finance business, risk management has become a core issue for trust companies. The trust industry is facing complex risk challenges brought about by the expansion of the consumer finance market - the combination of small, high-frequency, and diversified characteristics and the trend of excessive overconsumption have led to a significant increase in credit risk, operational risk, and market risk. Coupled with stricter regulation and intensified market competition, the pressure of risk management has become prominent. This study integrates the entire process mechanism of risk identification, assessment, and response, constructs a multi-source data fusion risk identification system, optimizes the quantitative and qualitative risk assessment model (such as the integration of Analytic Hierarchy Process and Monte Carlo simulation), designs dynamically adjusted risk response strategies, and verifies the effectiveness of the strategies through case studies and effect evaluations. The results show that although H Trust Company has established a full process system covering risk identification, assessment, and response, there is still room for improvement: risk identification overly relies on subjective experience and team discussions, lacking data support; The evaluation process is programmatic for the application of Analytic Hierarchy Process and does not dynamically adapt to the characteristics of consumer finance business; The response measures did not fully match the business characteristics (such as due diligence using traditional credit models and blind spots in monitoring fund flows). The systematic risk management framework constructed in this study theoretically integrates the risk characteristics of consumer finance with trust operation logic, and provides operational tools and process optimization solutions in practice, promoting the improvement of risk prevention and control capabilities of trust companies and the high-quality development of the consumer finance industry. In the future, it is necessary to deepen research on diversified risk types, promote dynamic strategy adjustments, and integrate differentiated strategies with new technologies.
Consumer finance, trust companies, risk management, full process system, dynamic strategies.
Li Fang. Research on Risk Management Strategies in the Trust Industry under the Background of Consumer Finance Transformation. International Journal of Social Sciences and Economic Management (2026), Vol. 7, Issue 1: 97-104. https://doi.doi.org/10.3807/IJSSEM.2026.070111.
[1] Hui, X. (2026). Research on the Design and Optimization of Automated Data Collection and Visual Dashboard in the Medical Industry. Journal of Computer, Signal, and System Research, 3(1), 27-34.
[2] Shen, D. (2026). Application of Large Language Model in Mental Health Clinical Decision Support System. International Journal of Engineering Advances, 3(1), 23-30.
[3] Wang, Y. (2026). Research on Optimization of Neuromuscular Rehabilitation Program Based on Physiological Assessment. European Journal of AI, Computing & Informatics, 2(1), 21-30.
[4] Ding, J. (2026). Optimization Strategies for Supply Chain Management and Quality Control in the Automotive Manufacturing Industry. Strategic Management Insights, 3(1), 17-23.
[5] Zhang, Q. (2026). How to Improve Marketing Efficiency and Precision through AI-Driven Innovative Products. Strategic Management Insights, 3(1), 1-8.
[6] Liu, Y. (2026). The Promoting Role of Fintech and Product Innovation in the Context of the Digital Economy. Strategic Management Insights, 3(1), 9-16.
[7] Lu, C. (2026). Research on 3D Reconstruction Methods of Remote Sensing Images Combined with Deep Learning and GIS. International Journal of Engineering Advances, 3(1), 15-22.
[8] Cai, Y. (2026). Design and Implementation of System Extensibility under High Concurrency Environment. International Journal of Engineering Advances, 3(1), 31-37.
[9] Liu, Y. (2026). The Application of Data-Driven Financial Risk Management in Multinational Enterprises. Economics and Management Innovation, 3(1), 20-26.
[10] Huang, J. (2026). Practice of Public Space Optimization and Functional Enhancement in Cultural Architecture. European Journal of Engineering and Technologies, 2(1), 9-21.
[11] Xu, D. (2026). AI-Driven Video Content Optimization Strategies for Immersive Media. European Journal of Engineering and Technologies, 2(1), 1-8.
[12] Qi, Y. (2026). AI Driven Payment System Security Improvement and User Privacy Protection Mechanism. Journal of Computer, Signal, and System Research, 3(1), 35-41.
[13] Qi, Y. (2026). High Reliability Architecture and Compliance Design of Enterprise Level Financial Infrastructure. International Journal of Engineering Advances, 3(1), 8-14.
[14] Sun, J. (2025). Research on Financial Systemic Risk Measurement Based on Investor Sentiment and Network Text Mining. Socio-Economic Statistics Research (2025), 6(2), 185-193.
[15] Lu, Z. (2025). Design and Practice of AI Intelligent Mentor System for DevOps Education. European Journal of Education Science, 1(3), 25-31.