Financial Services & Banking
Model risk, compliance, and audit-ready AI on one governed platform.
Banks and capital-markets firms run hundreds of models and, increasingly, AI agents that touch customer data, trading systems, and external services. AIXYRA brings model risk management and AI governance into one system of record alongside the platforms and data sources they depend on.
Regulatory drivers
- EU AI Act — high-risk classification for credit scoring and creditworthiness systems
- Model risk management expectations (e.g., SR 11-7-style supervisory guidance)
- GDPR for automated decision-making and customer data
- NIST AI RMF and ISO/IEC 42001 for enterprise AI assurance
The governance challenge
Model risk teams track inventories in spreadsheets with no view of how a model connects to the data sources, tools, and services feeding it — and no cascade view when one upstream component drifts or is deprecated. Agentic assistants add tool and external-service access that bypasses traditional model inventories entirely.
How it works in practice
A bank deploys an agentic AI assistant for credit adjudication. It calls an internal scoring model, pulls from a customer-data source, and reaches an external bureau through an MCP server. In AIXYRA, each entity is registered; the MCP server must clear an approval checkgate before the agent can use it; the architecture graph renders the full dependency chain; and risk scoring propagates the bureau's rating up to the agent — so the model risk committee reviews one auditable picture instead of five disconnected ones.
Illustrative scenario. AIXYRA does not imply any specific customer engagement.
Outcomes for Financial Services & Banking
The AIXYRA capabilities behind this solution
AI Model Catalog
Catalog AI models across providers with provenance tracking, version management, and automated governance approval ensuring models meet compliance requirements.
Risk Scoring with Dependency Propagation
Automated risk assessment that propagates scores through the 6-layer architecture dependency chain, revealing how risks cascade across interconnected AI systems.
Compliance Frameworks
Map AI systems against EU AI Act, NIST AI RMF, ISO/IEC 42001, GDPR, and custom frameworks with automated assessments and audit-ready evidence.
MCP Server Registry
Govern Model Context Protocol servers with approval workflows purpose-built for the agentic AI era, ensuring MCP tools meet security and compliance standards.
Audit Reports
Generate compliance documentation and audit evidence on demand, demonstrating governance posture to regulators, auditors, and executive stakeholders.
Architecture Dependency Graphs
Interactive 6-layer dependency visualization showing relationships between agents, models, tools, data sources, platforms, and services for complete traceability.