Risk & compliance.

Challenges

Why data matters

Growing regulatory requirements (BCBS 239, Solvency II, DORA, AI Act, GDPR): compliance now relies on data quality, traceability and governance.

What data brings

What data changes

Reliable risk data

Lineage, quality checks and reconciliation of data feeding models and regulatory reports.

Fraud detection

Detection models for payments, claims or transactions.

Risk management

Operational, credit and market risk dashboards.

Personal data compliance

Processing mapping, consent management, anonymisation.

AI governance

Model inventory, risk assessment and documentation required by the AI Act.

Generative AI

First generative AI use cases

• Analysis of regulatory texts and impact identification
• Assisted KYC checks and document review
• Writing compliance documentation

Method

The Visian approach

1. Framing with the department concerned: decisions to inform, indicators, priorities.
2. Data : source inventory, quality measurement, corrections at source.
3. Tool : dashboard, model or AI assistant delivered in short iterations.
4. Adoption : team training, usage tracking, skills transfer.

References

Visian achievements

• BNP Paribas: payment fraud journey redesign, IRBA modelling, operational risk dashboards
• Société Générale: risk data lake
• BPCE / Natixis: risk data engineering and credit risk portal
• Servier: GRC cybersecurity expertise

All references