Risk Control and Data Application in the Bulk Commodity Industry: Challenges, Solutions, and Core Capabilities

Ant Group, an internet‑centric enterprise, investigates whether its existing data capabilities can be extended to serve the traditional bulk commodity industry, helping it achieve digital upgrades and implement risk‑control scenarios.

Industry background : Unlike consumer‑oriented C‑end scenarios, bulk commodities involve large‑scale B2B transactions with highly dispersed data, leading to distinct market, operational, and credit risks. Ant conducted extensive research with major traders such as Jianda Group, Xiamen International Trade, and Xiangyu Co., discovering that traditional business relies heavily on expert experience, while digital transformation demands data‑driven decision making.

Data‑application pain points : (1) Information aggregation is difficult due to fragmented, unstandardized, and unstructured data (paper contracts, media files). (2) Value recreation is hard because of complex data processing and relationship extraction. (3) Intelligent decision‑making is challenging without deep industry expertise and resources.

Core capabilities : Ant has built a "data + model + platform" solution that integrates multi‑source data, constructs industry‑specific credit‑risk dimensions (e.g., shell companies, false enterprises), and supports risk monitoring across three business lines: front‑line field inspection tools, a one‑stop risk‑management platform for middle‑office teams, and fine‑grained operational tools for senior management.

Data asset construction : The approach consolidates external data (business registration, judicial, tax, IP) and internal client data, creates a multi‑data hub for integration, and performs entity resolution to map data to enterprises, industries, and individuals, forming a structured data warehouse and industry‑specific tag pool.

Indicator framework : Indicators are divided into public data (e.g., registration), client‑provided data (e.g., financial statements), and machine‑learning‑derived metrics generated from collaborative models.

Quantitative modeling : Based on the indicator system, Ant develops fraud, admission, classification, and credit‑limit models. These models are built with industry‑aware features, ensuring explainability rather than black‑box outputs, which is essential for traditional sectors.

Data security and privacy : To address heightened data‑security concerns, Ant separates the architecture into a client domain (distributed nodes for data collection and fusion) and an Ant Shield domain (centralized processing). The system leverages privacy‑preserving computation, distributed decision engines, and data‑quality guarantees to securely fuse client and Ant data.

Overall, the solution demonstrates how a data‑centric, AI‑enabled, and security‑aware platform can empower the bulk commodity industry with risk‑aware digital transformation.