SafeScore

Redefine Credit Risk Modeling through Automation and Regulatory Excellence

Fully regulated models with rapid, data-driven iterations. Transparent, explainable, and collaborative framework for compliance.

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SafeScore

Integration to various databases
SafeScore allows you to import data from various resources such as oracle sql, postgresql, mysql and csv.
Missing value replacement
SafeScore replaces missing values with imputation methods and if your sample is not large enough, oversampling is available.
Feature engineering and eliminations
New variables can be created, and features can be eliminated via gini coefficient, PSI or information value. Interaction between variables is visualized in correlation matrix.
Multiple Model Creation and Sharing
Multiple models can be created and shared with colleagues. Hence, it is possible to create multiple models with different techniques and variables to find the best model. Based on model, a scorecard is produced to explain the model.

Why SafeScore?

Redefining Credit Risk Modeling through Automation and Regulatory Excellence. Developed through Rapid, Data-Driven Iterations for Continuous Improvement and Compliance

Fully regulated models

SafeScore ensures that every stage of credit risk modeling — from data preparation to model deployment — is fully aligned with regulatory standards. Compliance is built into the process, not added afterward.

Rapid, Data-Driven Model Development

With fast iterations directly on data, SafeScore enables continuous model refinement. Variable engineering, automated missing value imputation, and advanced binning techniques accelerate development without sacrificing accuracy.

Transparent and Explainable Model

SafeScore provides full visibility into model logic and variable behavior through WoE optimization, IV/PSI checks, and correlation analysis. Each model is statistically robust, interpretable, and ready for regulatory review.

Collaborative Framework

Models can be easily shared, benchmarked, and compared across teams. Built-in features like VIF control, stepwise selection, and model comparison (e.g., Logistic Regression vs. Boosting) promote consistency and transparency in development.