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How Finalyse can help
Credit Risk Modelling
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.
Key Features
- 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.
- Finalyse Machine Learning Model Validation Framework will help to ensure that your model performs as expected.
Nemanja Djajić is a Managing Consultant with more than 12 years of experience in credit risk modeling and data science area. He gained his experience through multiple roles in banking industry including the position of data science department director in one of the biggest banks in Eastern Europe. Nemanja’s main area of expertise lies within the development and validation of risk and business related models, using traditional or machine learning methodologies.
Gergely Tréfa is a Senior Consultant at Finalyse with more than 5 years experience in credit risk model development, validation and data science. He has extensive experience in developing and validating PD, LGD, EAD models under IRB and IFRS frameworks. Gergely holds a Master's in Quantitative Finance and is proficient in R, Python, and SQL. In addition to regulatory modeling, he has developed non-regulatory machine learning models, such as collection and prepayment models, and has applied NLP techniques to automate risk reporting processes.
