Dxm Eros: A Massive‑Scale Graph Platform for Financial Risk Control

The presentation begins with an overview of fintech development stages, highlighting the shift from basic informationization to deep integration of big data, cloud computing, AI and blockchain in financial risk management.

It then describes the evolution of financial risk‑control techniques, moving from manual review to rule‑based systems, machine‑learning models, and finally knowledge‑graph approaches that capture relationships among entities.

The core of the article is the Dxm Eros graph platform. The framework consists of a massive heterogeneous data pool, high‑performance compute clusters, and a scalable graph database capable of storing billions of vertices and edges. The platform provides unified query and analysis, visual exploration, and a library of classic and custom graph algorithms.

Key modules are detailed:

Storage module: supports trillion‑scale graphs with millisecond‑level latency and integrated query‑analysis.

Analysis module: offers vertex, edge, community algorithms and graph‑representation learning, including GraphSAGE and GAT hybrids.

Modeling module: AutoGraph automates graph feature generation, incremental training and heterogeneous graph support, enabling end‑to‑end model building without deep technical expertise.

Visualization module: interactive layouts, node/edge statistics, community detection, and one‑click risk tagging for fraud, AML and audit scenarios.

Application scenarios cover the entire loan lifecycle—smart acquisition, anti‑fraud, risk scoring and post‑loan monitoring—catering to algorithm engineers, strategy teams, credit reviewers and AML analysts.

Two case studies illustrate the platform’s impact: a credit‑data graph with over a billion nodes that improves feature richness and a risk‑control graph (12 billion vertices, 80 billion edges) that yields more than a 1 % lift in AB‑card performance.

Additional functionalities include intelligent anti‑fraud (graph‑based risk scoring, community detection), anti‑money‑laundering (transaction path tracing, structural anomaly detection) and smart audit (OCR + NLP + knowledge‑graph for instant document verification).

The article concludes with a summary and outlook, emphasizing the goal of lowering graph‑learning barriers, enriching visual tools, and delivering industry‑wide solutions so that more practitioners can leverage graph technology for risk mitigation.