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Banks, insurers, asset managers, payments and fintech.
Financial Services
AI operating inside regulated processes, dependent on sensitive data and supervised technology estates.
The challenge
What makes governance hard here.
- AI is being adopted across credit, financial crime, servicing and trading faster than model governance can keep pace.
- Model risk, data quality and operational resilience obligations are managed in separate registers.
- Supervisory questions arrive across domains, but evidence is assembled manually from disconnected systems.
How Regurai helps
What changes with a connected model.
- Register AI systems, models and agents alongside the processes, applications and data they depend on.
- Connect model risk, control coverage and regulatory obligations to the same underlying inventory.
- Assemble defensible evidence for a decision without re-collecting it from each domain team.
Use cases
Decisions Regurai supports in this sector.
Assessing connected risk before deploying an AI model into a customer-facing process
Understanding which AI systems depend on sensitive or low-quality data
Evaluating build, buy or deploy options with explicit economic assumptions
Preparing traceable evidence for internal audit or supervisory review
Relevant capabilities
Capabilities most used here.
- AI & Model Inventory
- Real-Time Risk Scoring
- Regulatory Obligation Mapping
- Data Lineage & Sensitivity
- AGEE Economic Modelling
- Audit Trail & Evidence
Governance considerations
What oversight typically focuses on.
- Model risk management ownership, validation state and lifecycle evidence
- Operational resilience dependencies for important business services
- Data protection and sensitivity classification for AI training and inference data