Consulting

Model Risk & Model Validation

Showing that models used in critical processes such as credit decisions are accurate, fair and explainable is increasingly a requirement. As expectations on model risk and validation grow in banking, we validate your models independently and strengthen your model risk management process.

What we offer

  • Independent validation of credit scoring, risk and fraud models
  • Performance, stability and discriminatory power testing
  • Bias (fairness) analysis and explainability
  • Model documentation and model inventory
  • Designing model monitoring and revalidation processes

When you need it

  • You suspect model performance has degraded over time
  • Audit or regulation requires independent validation
  • You use models in areas classed as high-risk under the EU AI Act, such as creditworthiness assessment

How we work

  1. Scope

    We define the models and criteria to validate.

  2. Analyse

    We test the data, methodology and performance.

  3. Report

    We report findings and improvement recommendations.

  4. Monitor

    We set up ongoing monitoring and revalidation.

What you get

  • Independent validation report
  • Performance and bias test results
  • Improvement recommendations
  • Monitoring indicators and process document

Related courses

3–5 days

Applied Machine Learning

Classification, regression, model evaluation and feature engineering on real-world datasets.

Learn more →
1 day

Responsible AI, Regulation & Data Security

The EU AI Act, Turkish data protection law, Türkiye's AI Action Plan, model risk and privacy: a framework for deploying AI in a compliant and trustworthy way.

Learn more →
3 days

MLOps: Taking Models to Production

CI/CD, model versioning, monitoring and data drift, plus LLMOps for LLM applications: keeping models reliable in production.

Learn more →

Frequently Asked Questions

Why is independent validation important?

An assessment independent of the development team uncovers overlooked risks and gives assurance for audit and regulatory expectations.

What information is needed?

Model documentation, development and validation data (or access to it) and the model's production outputs. A working setup that meets confidentiality requirements is agreed together.

Can machine learning models be validated too?

Yes. As well as classic scorecards, tree-based and other machine learning models can be validated, including explainability analysis.

How often should validation happen?

It depends on your model risk policy. Good practice is ongoing monitoring plus periodic revalidation and revalidation after significant changes.

Free assessment · 3 min

EU AI Act: Am I in Scope?

Is your AI system in scope of the EU AI Act, which risk category does it fall into, and which obligations apply from when?

Start now

Related articles

Let's talk about your project

Tell us about your training, consulting or software needs and we will get back to you within 2 business days.