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.
What Finalyse experts say
On the 27th of October 2021 the European Commission has released its proposal of a new banking package, that (among other things) implements the first pillar of Basel IV into the banking regulation. This package introduces a big change to the European Banking industry. The combination of changes to the credit risk (which is the main risk driver in the EU), and output floor will be particularly strongly felt across the EU.The new requirements are computationally difficult and institutionally challenging. They require variety of tools and data all of which will be costly. The current release is a Commission proposal only and still needs to go through the council and the parliament before its release in the official Journal of the EU. However, it is not expected to undergo very many additional changes and its adoption should be smooth.
ReadThis article discusses the benefits of applying advanced analytics in the development of behavioural scoring models. It investigates how Machine Learning techniques can be used to model the behavioural scores of consumers in each step of the development and model assessment. Some concerns regarding the usage of Machine Learning in behavioural scoring models are addressed.
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