Big Data Risk Control System for Textile Industry Supply Chain Finance

The textile industry, a cornerstone of China's economy, features a long and complex supply chain from raw cotton and fibers to finished garments, exposing it to policy, industry, operational, and moral risks that challenge financial services and risk management.

The presentation outlines a "1+D" big‑data risk‑control model: "1" represents the credit subject (enterprise or individual), "D" denotes internal and external data sources (IoT device data, procurement, sales, warehouse, corporate, judicial, and financial flow data), and "+" signifies risk‑control levers such as information, fund, commerce, and logistics flows.

A four‑layer data foundation is described: data sources, a basic layer that processes raw data into services (e.g., 360‑degree profiles, feature libraries, knowledge graphs), a data‑product layer offering dashboards, alerts, model centers, and AI services, and an application layer for specific business scenarios.

Model and rule libraries are built according to the "1+D" framework, providing credit‑subject scoring models, transaction‑capture components, and data‑driven atomic models for capacity prediction, risk transmission, and corporate information.

The configurable risk‑control system includes a workflow engine to handle diverse credit‑subject processes and a rule engine for rapid deployment, testing, and back‑testing of credit, marketing, and post‑loan rules.

An application case demonstrates IoT‑enabled monitoring of a weaving factory, using machine speed, power, and runtime data to predict material demand, set dynamic credit limits, and detect operational anomalies for loan monitoring and capacity matching.

Finally, the article discusses opportunities—government policy support, accelerated industrial IoT adoption, and new data‑driven financing models—and challenges such as low digitalization, limited data samples, and the need for more expertise and talent in the textile sector.