Applied Machine Learning
Classification, regression, model evaluation and feature engineering on real-world datasets.
Learn more →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.
We define the models and criteria to validate.
We test the data, methodology and performance.
We report findings and improvement recommendations.
We set up ongoing monitoring and revalidation.
Classification, regression, model evaluation and feature engineering on real-world datasets.
Learn more →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 →CI/CD, model versioning, monitoring and data drift, plus LLMOps for LLM applications: keeping models reliable in production.
Learn more →An assessment independent of the development team uncovers overlooked risks and gives assurance for audit and regulatory expectations.
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.
Yes. As well as classic scorecards, tree-based and other machine learning models can be validated, including explainability analysis.
It depends on your model risk policy. Good practice is ongoing monitoring plus periodic revalidation and revalidation after significant changes.
Is your AI system in scope of the EU AI Act, which risk category does it fall into, and which obligations apply from when?
The EU AI Act sorts AI systems into four risk categories. What each means, classification examples from banking, HR and marketing, and the high-risk exceptions.
Read more →The EU AI Act covers not only EU companies but any AI system whose output is used in the EU. Scope, risk categories, the updated 2026 timeline and penalties for Turkish companies.
Read more →Rule-based fraud systems can't keep up with new schemes. How machine learning improves fraud detection: approaches, imbalanced data, the right metrics and hybrid architectures.
Read more →Tell us about your training, consulting or software needs and we will get back to you within 2 business days.