Lena Mertens

January 14, 2026

Re-thinking Model Inventories for ESG, AI and Financial Crime

And how Yields Supports Consistent Governance Across Non-Traditional Models

January 14, 2026

The broadening of Model Risk Management (MRM) within the banking sector is no longer just a regulatory trend; it is a structural necessity. As banks move beyond traditional credit and market risk models to incorporate ESG, AI/ML, and Financial Crime (AML/Fraud) models, the “static” inventory is becoming a liability.

Within the Yields Model Risk Management software, this shift is addressed through Yields for Governance, the module that defines how models are structured, owned and governed across domains.

The Deloitte 2025 EMEA MRM Survey highlights that while banks are making clear progress in inventory management and governance, significant maturity gaps remain, especially when dealing with non-traditional model behavior.

The Configurable Inventory: A Strategic Imperative for Banks

For banks, a model inventory is the “cornerstone of effective MRM”. However, traditional inventories designed for linear, statistical models cannot effectively manage the multi-dimensional nature of modern banking models.

Yields for Governance takes a different approach. Instead of enforcing a single, fixed inventory structure, it provides a configurable governance layer that adapts to the nature of the model and the risk domain it operates in.

1. The ESG Data Complexity

The survey reveals that 75% of banks are now using ESG models, nearly doubling since 2023. Unlike traditional financial models, ESG validation requires capturing diverse data points such as carbon footprint impact, physical climate risks, and unique Pillar 2 metrics.

A configurable inventory like the one provided by Yields allows banks to govern ESG models within the same MRM framework:

Dynamically define inventory fields
Yields for Governance enables banks to configure ESG-specific attributes such as carbon exposure or green asset ratios without re-engineering the underlying inventory.

Centralise ESG metadata
ESG-related information is maintained alongside traditional risk data, avoiding parallel tools and reducing fragmented governance.

Adapt to evolving regulatory expectations
Inventory fields can be adjusted as ESG requirements change, without redesigning governance structures or disrupting existing models.

2. The AI/ML Lifecycle Challenge

AI and ML models are increasingly used in credit decisioning, pricing and fraud detection. At the same time, regulatory scrutiny from the EU AI Act and supervisors such as the ECB and PRA continues to increase.

Unlike traditional models, AI systems can retrain, drift and change behaviour over time. This requires inventories that go beyond static registration and actively support AI-specific governance.

Within Yields, this is addressed through a configurable inventory that supports the full AI model lifecycle:

Ensure traceability and explainabilityYields for Governance allows banks to capture AI-specific indicators such as explainability metrics, data integrity controls and monitoring signals, supporting transparency and supervisory expectations.

Apply differentiated validation workflows
AI and ML models often require validation approaches that differ from traditional statistical models. Yields enables tailored validation and approval workflows per model type, while keeping them within a single MRM framework.

3. Financial Crime and Operational Risk Models

Financial Crime models such as sanction screening and fraud detection operate under different constraints than traditional risk models. Limited historical data, rapidly changing patterns and high operational impact require a governance approach that emphasises continuous oversight rather than periodic validation.

Within Yields, Financial Crime models can be governed through a configurable inventory that supports their specific risk profile:

Explicit model classification
Yields allows banks to flag Financial Crime and operational risk models within the inventory, ensuring they are assigned to specialised teams with the appropriate expertise and oversight responsibilities.

Support continuous monitoring
Configurable dashboards integrate transaction monitoring signals and behavioural indicators, enabling early detection of anomalies such as sudden spending changes or suspicious locations.

Orchestrating Compliance with Yields

The shift away from unmanaged spreadsheets to structured, configurable tooling is accelerating. For a bank, the value of an orchestration platform like Yields lies in its ability to:

Integrate via External API: Support custom integrations for Documents, Entities, Findings, and Models to create a unified view of risk.
Operationalize Governance: Clearly define and document the role of the Model Owner, which is critical for accountability.
Scale Efficiently: Whether a small credit union averaging 18 models or a large Tier 1 bank with nearly 500, Yields adapts to the specific risk profile and operational needs.

If you would like to know more about Yields Model Risk Management solution. Let’s get in touch!

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Jos Gheerardyn has built the first FinTech platform that uses AI for real-time model testing and validation on an enterprise-wide scale. A zealous proponent of model risk governance & strategy, Jos is on a mission to empower quants, risk managers and model validators with smarter tools to turn model risk into a business driver. Prior to his current role, he has been active in quantitative finance both as a manager and as an analyst.

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