AI Investigator for financial crime and compliance
Agent-driven investigation embedding Lucinity's AI-native capabilities into Oracle's financial-crime platform to support investigator workflows
- Who we serve
- Private Sector
- Category
- Financial services & insurance
- Value
- Risk management
- Human role
- Human approves every decision
- Maturity
The problem
Without AI Investigator for financial crime and compliance, banks and insurers rely on sampling and after-the-fact review, while the signals in transactions, alerts, case data sit unread, so problems surface only after the loss, incident or complaint.
How it works
AI Investigator for financial crime and compliance - Agent-driven investigation embedding Lucinity's AI-native capabilities into Oracle's financial-crime platform to support investigator workflows. The approach relies on LLM agents, graph analytics. It draws on transactions, alerts, case data and is typically owned by banks and insurers. The main constraint is model risk; regulator expectations. Where the human comes in: investigators decide on filings and escalations. It has been announced without a named production use yet; named deployments include Banks using Oracle financial-crime software.
Where the human comes in
Investigators decide on filings and escalations
What it needs
Transactions, alerts, case data
What to plan for
Model risk; regulator expectations
In the field
Banks using Oracle financial-crime software
S18
Public deployments by other organizations, listed for context. Not Sphere projects.