EU AI Act pressure

Creditworthiness and fraud‑related AI systems are classified as high‑risk. Institutions must demonstrate robust risk management, documentation, logging, and human oversight for these models.

Fragmented model governance

Many organisations manage model inventories in spreadsheets, documentation in static files, monitoring in separate dashboards, and approvals via email or tickets.

Rising model risk scrutiny

Supervisors and audit teams expect clear ownership, evidence of monitoring and validation, and traceable change histories for material models – especially in credit and fraud.

Central model inventory

Maintain a single, living registry of your credit and fraud models:

• Discover and register models from your existing ML platforms.
• Capture ownership, purpose, datasets, environments, and status.
• Tag systems by EU AI Act risk category and internal materiality.

Automated documentation packs

Generate and maintain AI‑Act and model‑risk‑grade documentation without starting from a blank page:

• Standard templates for system and model description, data, validation, risks, and monitoring.
• Auto‑filled sections from model metadata and metrics.
• AI‑assisted draft text, always reviewed and approved by humans.
• Versioned documents that stay in sync as models evolve.

Monitoring & lifecycle logging

Connect your monitoring outputs and keep a complete history:

• Record deployments, retrains, threshold changes, and incidents.
• Attach performance and drift metrics from your existing dashboards.
• View a clear timeline of what happened to each model and why.

Workflows & approvals

Standardise governance across teams:

• Pre‑configured workflows for initial approval, periodic review, and changes.
• Task lists for model owners, validators, risk, and compliance.
• Full audit trail of decisions and their supporting evidence.

Portfolio and reporting views

Give leadership and auditors the visibility they need:

• At‑a‑glance overview of all high‑risk AI systems and their status.
• Flags for missing documentation or overdue reviews.
• One‑click export of model packs and portfolio reports.

Integrations

• Connects to ML registries and pipelines (e.g. MLflow/Databricks, SageMaker, H2O, in‑house tools).
• Links to your ticketing and documentation tools (e.g. Jira, Confluence, shared drives).

Security & deployment

• Runs in a secure, single‑tenant environment.
• Designed to work primarily with metadata and metrics by default.
• Flexible deployment options in your cloud or on‑premises.

Banks & lenders

Using ML for credit decisions, fraud detection, and collections, and facing growing AI‑related regulatory expectations.

Fintechs & BNPL providers

Scaling their model portfolios and needing a pragmatic way to document, monitor, and govern AI systems.

Risk & compliance teams

Model‑risk, compliance, and audit teams who need real control and evidence without slowing down analytics.