ing-bank/skorecard
scikit-learn compatible tools for building credit risk acceptance models
This tool helps credit risk analysts and data scientists streamline the development of credit risk acceptance models, commonly known as scorecards. It takes raw customer financial data and automatically sorts it into meaningful groups (bins), making it easier to build and evaluate models that predict creditworthiness. The output is a well-structured credit risk model ready for decision-making.
110 stars. Used by 1 other package. No commits in the last 6 months. Available on PyPI.
Use this if you need to build or refine traditional credit scorecards for banks and financial institutions, especially if you want to automate the data binning process while still allowing for expert adjustments.
Not ideal if you are looking to build highly complex, black-box machine learning models that don't rely on binned features or logistic regression principles.
Stars
110
Forks
31
Language
Python
License
MIT
Category
Last pushed
Feb 09, 2025
Commits (30d)
0
Dependencies
7
Reverse dependents
1
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/ing-bank/skorecard"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
xRiskLab/xBooster
Explainable Boosted Scoring with Python: turning XGBoost, LightGBM, and CatBoost into...
minerva-ml/open-solution-home-credit
Open solution to the Home Credit Default Risk challenge :house_with_garden:
semasuka/Credit-card-approval-prediction-classification
Credit risk analysis for credit card applicants
ParthS007/Loan-Approval-Prediction
Loan Application Data Analysis
Algoritmica-ai/deeploans
Deeploans is an open-source framework for processing European loan-level data, offering tools...