Risk Control in the Bulk Commodity Industry: Data‑Driven Solutions and Credit‑Risk Modeling by Ant Group

Ant Group explores how its data‑building expertise, originally developed for internet C‑end scenarios, can be applied to the traditional bulk commodity (B2B) industry to support digital upgrade and risk‑control applications.

Background of bulk‑industry risk control – Unlike consumer‑oriented platforms such as Taobao and Alipay, bulk‑commodity trading involves large‑scale B2B transactions with highly fragmented data, making risk assessment fundamentally different. Ant conducted extensive research with leading traders (e.g., Jiafa Group, Xiamen International Trade, Xiangyu Co.) and found that traditional risk management relies heavily on expert experience, while the industry now seeks data‑driven decision support.

Key risk types in the bulk sector

Market risk – caused by pandemic impacts, international instability, supply‑demand fluctuations, liquidity and exchange‑rate changes.

Operational risk – stemming from complex contracts, quality control, logistics, and other B2B transaction elements.

Credit risk – the need to evaluate upstream suppliers and downstream customers to ensure transaction security.

Data‑application pain points

Difficulty in information aggregation due to scattered, non‑standardized, and unstructured data (paper contracts, scanned files, audio‑video).

Challenges in value recreation because of complex data processing, relationship extraction, and high‑value information extraction.

Limited intelligent decision‑making because industry‑specific resources and expertise are scarce.

Core capabilities for bulk‑industry data application

Ant has built a "data + model + platform" solution that integrates multi‑source macro‑ and micro‑data, constructs industry‑specific credit‑risk dimensions (e.g., shell companies, fake state‑owned enterprises), and develops data models for merchant admission, classification, credit‑limit allocation, and real‑time contract‑risk monitoring.

Three‑layer business enablement

Front‑line sales: mobile‑based due‑diligence tools that capture on‑site information, automatically recognize financial statements and images, and fuse unstructured field data with online data.

Mid‑office risk management: a one‑stop risk‑management platform that aggregates merchant data, visualizes risk indicators, and codifies expert knowledge into reusable processes.

Enterprise‑level operation: data‑driven fine‑grained operation, e.g., differentiated credit limits based on data analysis rather than uniform expert judgment.

Data‑asset construction

Integration of massive external data (business registration, judicial, tax, IP) and internal client data.

Data integration via a multi‑source data hub that securely links and merges heterogeneous datasets.

Entity‑level data consolidation into a data‑warehouse‑style asset layer, forming industry‑specific tag pools.

Indicator framework

Indicators are divided into three categories: public data (e.g., registration, judicial), client‑provided data (e.g., financial statements), and Ant‑generated model metrics (derived from machine‑learning algorithms and expert‑driven models).

Quantitative modeling

Based on the indicator system, Ant builds quantitative models such as fraud detection, merchant admission, classification, and credit‑limit models. The process includes industry classification, indicator decomposition (operational, relational, qualification, public‑opinion, compliance), and label‑driven data analysis to ensure model interpretability.

Data security and privacy

Given heightened data‑security regulations, Ant addresses data‑privacy concerns by separating client‑side and Ant‑side data domains, deploying distributed nodes for client data processing, and a centralized "Ant Shield" node for secure computation, leveraging privacy‑preserving computing, distributed decision engines, and data‑quality guarantees.

Conclusion

The presented solution demonstrates how Ant Group leverages data integration, AI‑driven modeling, and secure computing to enable risk control and digital transformation for traditional bulk‑commodity enterprises.