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Credit Risk Modelling

The highest possible predictive power of your models
Designed to help Credit Risk Modelling Units

Bank’s competitive advantage to a great extent depends on modernity of models in use. Finalyse offers support in methodology choices, the development of scoring models for credit decisioning and monitoring purposes and their deployment. These models are built with respect for industry-leading methods, including Machine Learning techniques.

How does Finalyse address your challenges?

We take accountability for the entire model development process – starting from data preparation through exploratory data analysis and ending with model development and its validation on independent sample.

Holding true for all recommendations from European and local authorities, we guarantee the regulatory compliance of the model developed.

Control your model risk with Finalyse Credit Risk Modelling toolkit to bridge the talent gap and reduce implementation risks. 

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Key Features

  1. A model developed to fit your needs. Having completed numerous model development projects for all kinds of institutions across Europe, Finalyse leverages on its vast experience and tailors the offer to each individual client.
  2. Finalyse Machine Learning Model Validation  Framework will help to ensure that your model performs as expected.

Our subject matter experts

Can Soypak
Principal Consultant - Credit Risk Modelling / Credit Risk Model Validation Expert

Can Soypak is a Principal Consultant based in Finalyse Amsterdam with extensive track record in quantitative risk management. Can has successfully delivered and managed several projects on credit risk model development and validation for various regulatory purposes (IRB, IFRS 9, Economic Capital, ICAAP, etc.) across different geographies and different portfolios. He is currently assisting several European banks with the improvement of IRB/IFRS 9 models integrating the latest regulatory requirements.

Consultant - Credit Risk Modelling Expert

Long Hai Le is a Consultant with expertise in Credit Risk Modelling, Risk Data Analytics, and Risk Data Programming. He has a great exposure to multiple areas of credit risk from IFRS 9 and IRB to Retail Analytics and Regulatory Reporting. He is highly proficient in a wide range of programming languages such as: R, Python, SAS, etc. Long is a data savvy in Risk Management field.