AI​​-Enhanced Modeling of Probability of Default

Introduction:

For this client in the financial sector, the existing credit risk decision process for Probability of Default (PD) was based on a single, simplified model maintained in an individual analyst environment. This setup limited consistency, transparency, and the ability to reflect the full structure of retail and corporate portfolios. The project objective was to design and implement a comprehensive PD decision model that consolidates all critical risk modules and aligns with the bank’s governance and regulatory expectations with extreme gradient boosting algotithm.

Challenge – Simple PD Model and Fragmented Process:

Before the engagement, the bank relied on a basic decision model that used only a subset of available information and was operated in a separate, local environment.
There was no unified structure for application, deposit, financial, ​​Credit Bureau (BIK), and behavioral data, which resulted in:

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  Limited precision of PD estimates.
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  Difficulty in comparing and monitoring model performance across segments.
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  Higher operational effort whenever updates or validations were required.

Our Approach – Econometric Methods for PD Modeling:

To address these gaps, the team built a new PD scoring framework based on proven econometric methods. The solution focused on:  

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  Logistic regression with Weight of Evidence (WOE) transformations for stable and interpretable predictor behavior.
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  Dedicated tools for model quality assessment and stability analysis, including monitoring of discriminatory power and calibration.
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  A modular structure integrating all key components: application, deposit, financial, Credit Bureau, and behavioral modules, so that the PD model reflects the complete customer risk profile.