Intelligent Risk Control Algorithms for Logistics and Commercial Vehicle Finance

The demand for loans among small‑and‑micro enterprises in logistics and commercial vehicle finance is expanding rapidly, with the market share of such enterprises exceeding 95% of all registered entities in China and loan balances growing at a compound annual rate of over 12%.

Shiqiao Group focuses on serving this market through a four‑stage service model—purchase financing, logistics operation, vehicle maintenance, and vehicle replacement—achieving an 80% financial penetration in the commercial vehicle purchase segment, primarily via direct leasing and sale‑and‑lease‑back arrangements.

Traditional risk control in the logistics supply chain faces challenges such as heavy offline processes, large single‑loan amounts, poor data standardization, and difficulty acquiring fragmented data, while digital transformation creates new opportunities for quantitative risk assessment.

Intelligent risk control is presented as an inevitable industry trend, combining machine‑learning and deep‑learning algorithms (e.g., logistic regression, decision trees, XGBoost, neural networks) with a layered architecture that spans data acquisition, feature engineering, model building, and application across pre‑loan, in‑loan, and post‑loan stages.

Model selection balances interpretability and accuracy; supervised models dominate risk scoring, while unsupervised clustering aids in customer segmentation.

Feature‑portrait systems are emphasized as the cornerstone of effective models, involving data collection, cleaning, intermediate dataset construction, feature design (business‑logic, RFM, algorithmic extraction), and rigorous feature evaluation for coverage, discrimination, predictive power, and stability.

Graph‑based risk analysis is introduced to capture complex relational data among drivers, projects, vehicles, and companies, enabling rule mining, community detection, dynamic risk monitoring, and risk propagation tracking through node‑edge networks.

The overall framework integrates data pipelines, graph databases, and AI algorithms to build a comprehensive, automated, and scalable risk‑control solution for the logistics and commercial vehicle financing industry.