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Streamline your model development activities with the right tool
Designed to help Model Development and Validation Teams

The credit risk modelling toolkit provides a flexible platform to support your model development and validation teams (whether centralized or distributed across countries) to create compliant PD, LGD and EAD/CF models with IRB and IFRS9 standards and guidelines. The resulting model code can be version-controlled and implemented based on SAS Enterprise Minder technology without additional work or used to validate implementation. It is a transparent, customized environment that builds on decades of expertise and experience in modelling and SAS development accumulated at Finalyse.

How does Finalyse address your challenges?

Support for your development and validation of PD, LGD and EAD/CF models

Implementation  of the model codes

Reduce model risk, cost and time of development and maintenance

To get your questions answered

Key Features

  1. Tailored to your modelling methodologies and business requirements.
  2. Common interpretation of methodology and guaranteed compliance.
  3. Same toolkit for all modellers and validators.
  4. Easy to learn and roll-out.

What Finalyse experts say

27 Aug, 2019
The Language War in Credit Risk Modelling: SAS, Python or R?

The three languages were compared using a simple setup, as close as possible to a real-life situation. The exercise consisted in calibrating a logistic regression to identify loans likely not to be repaid on time in a sample dataset. The choice of logistic regression was driven by the fact that it is a simple but powerful approach still widely used in the industry.

01 Feb, 2018
IFRS 9 Expected Loss Model Validation

This expert input focuses on the validation of Expected Credit loss model validation; more specifically, it explains why it is a good idea now, after the scramble to have IFRS 9 compliant models in time, to consider validation. This input addresses the challenges of a methodological review of all models, and more specifically, it addresses the review of selected variables – macroeconomic factors, obligor characteristics, etc. It shows how to make the best use of the new ability to compare the outcomes of the models against the observed losses. In addition, this article tackles another challenge: the review of data quality – particularly of the modelling data set and new data.