The article details how a transaction platform replaced fragile manual reviews and static rule engines with a data‑flywheel, model‑distillation, and a multi‑agent AI system—automating labeling, dual‑modality risk detection, and anti‑corrosion mechanisms—to achieve a 99.96% reduction in annual financial loss.
This article explains a comprehensive AI‑powered loss‑prevention framework that automatically identifies financial‑risk scenarios in both existing and new code, integrates model‑based detection into product, development, testing, and release stages, and continuously refines coverage through intelligent monitoring and rule enforcement.
The paper introduces an AI‑driven loss‑prevention platform that augments manual risk analysis with automated field recognition to map database and code models, generate loss‑related methods and interfaces, and deliver pre‑emptive avoidance, real‑time detection, and post‑incident response, achieving over 1,200% growth in identified loss methods and near‑full field coverage.
The e‑commerce platform’s loss‑prevention architecture combines domain‑modeled scenario identification, pre‑emptive checks, automated testing, and a real‑time data‑reconciliation pipeline using Dcheck and rule factories to detect anomalies, trigger alerts, and execute emergency response plans, thereby minimizing financial risk and ensuring transaction stability.
YouZan’s real‑time loss‑prevention platform monitors database binlogs, transforms and verifies transaction data across five loosely coupled layers, handling 200 million daily messages and 60 million checks with dynamic sharding, caching and distributed locks to detect over‑charges, duplicate refunds, migration inconsistencies and unauthorized data changes.
