Core capabilities
01 | Fuse multi-source data into one risk view
Connect customers, accounts, transactions, contracts, companies, business records, and external risk information, organized by unified business objects as the base for risk analysis.
02 | Identify party relationships so hidden risk surfaces
Use entity recognition and relationship analysis to relate customers, companies, accounts, shareholders, and related parties, revealing transmission paths beyond a single entity.
03 | Build a risk ontology so AI understands financial business
Model parties, accounts, transactions, relations, events, rules, and risk metrics so AI understands risk in real business context rather than a single field or score.
04 | Support risk judgment that is evidenced and traceable
Combine rules, history, relationship networks, and risk events to analyze anomalies while retaining sources, paths, and rationale.
05 | Build risk agents that assist professional judgment
Specialist agents for identification, due diligence, relationship analysis, risk explanation, and strategy support help staff gather information, find issues, and form recommendations.
06 | Connect risk to action so control becomes a closed loop
Connect analysis results to review, recheck, tasks, approval, and tracking so discovery does not stop at alerts but enters subsequent handling and feedback.
FAQ
What information does asset management and risk analysis handle?
- Ledgers, contracts, corporate and legal records, transactions, guarantees, and historical disposal data—organized into a relationship network among parties, assets, and events.
How is risk judgment made traceable?
- The process keeps sources, relationship paths, and rationale so staff can review, rather than returning an unexplained score.
Can it connect to downstream disposal processes?
- Yes. Identification results can enter review, recheck, tasks, approval, and tracking instead of stopping on an alert page.