ML Credit Scoring System: Turnkey Implementation

Imagine a bank losing millions due to wrong credit decisions. Traditional logistic regression scorecards fail to capture nonlinear dependencies in data. One client using a linear model was rejecting 5% of good borrowers. We solve this with gradient boosting and deep learning. Machine learning in finance is our core. We build AI-driven credit scoring models that analyze hundreds of factors and deliver accurate default predictions. A 10 p.p. Gini coefficient increase saves the bank up to 10 million RUB on a 5 billion RUB portfolio. We also guarantee model accuracy and regulatory compliance, with a certified team of experts.

We implement machine learning-based credit scoring systems that replace outdated logistic regressions. Our experience shows: modern models with hundreds of features boost Gini by 8–15 p.p. compared to traditional approaches. An 8 p.p. Gini lift means millions of RUB annual savings for an average bank. Specific savings: a microfinance client saved 15 million RUB per year after implementation. Implementation costs start at 1 million RUB for a basic model, with ROI exceeding 5x in the first year.

Problem Statement and Data

Target variable: default within 12 months (binary classification) or PD for Basel III. Class imbalance: typically 1:10–1:50 (default vs. normal), requiring special attention. Creditworthiness assessment relies on heterogeneous data.

Feature sources:

How an ML Credit Scoring System Improves Accuracy?

XGBoost / LightGBM—the main workhorse. Handles tabular data, robust to outliers, deals with missing values, interpretable via SHAP. For most scoring tasks—optimal choice. XGBoost scoring delivers high accuracy, while LightGBM credit risk models are efficient for large datasets.

CatBoost—advantage when many categorical features (region, profession) without manual encoding.

Neural networks (TabNet, AutoInt)—gain on very large datasets (>5M records) with rich behavioral data. Harder to interpret and maintain.

Stacking. In production we often use ensemble: LightGBM + logistic regression (for interpretability in regulatory reporting) with a meta-learner.

Deep Dive: Handling Imbalance and Calibration

Precision 0.71 at recall 0.89 on class "default" due to 1:25 imbalance—common pain. Effective approaches:

Focal Loss. For neural networks significantly better than simple weighted cross-entropy. Parameter gamma=2 focuses training on hard examples, as described in Lin et al. (2017).

SMOTE with caution. Oversample + undersample works, but SMOTE on financial data may create unrealistic synthetic examples—must validate against business logic.

Calibration is critical. The model outputs a score, but we need a real default probability for LGD and EAD calculation, i.e., accurate PD estimation. Isotonic regression or Platt scaling after training. Check calibration curve—ideal: predicted probability 0.3 corresponds to 30% actual defaults.

from sklearn.calibration import CalibratedClassifierCV
from lightgbm import LGBMClassifier

base_model = LGBMClassifier(
    n_estimators=500,
    learning_rate=0.05,
    scale_pos_weight=25,  # class ratio
    min_child_samples=50
)
calibrated_model = CalibratedClassifierCV(
    base_model, method='isotonic', cv=5
)
calibrated_model.fit(X_train, y_train)

Why Data Drift Monitoring Matters?

Scoring models degrade over time. Causes: economic changes, credit policy shifts, borrower behavior changes.

Mandatory monitoring:

• PSI (Population Stability Index) on input features: PSI > 0.25—critical distribution shift, needs retraining.
• Gini coefficient on hold-out set monthly.
• Score distribution shift: if average score shifts >15 points—investigate.
• Outcome monitoring: after 6–12 months compare predicted vs. actual default rate.

Regulatory requirements in Russia are managed by the Central Bank. Key requirements: interpretability of rejection decisions (SHAP explanations), ability to dispute a decision, prohibition of certain features (discriminatory). GDPR-analogue—Federal Law 152—requires justification of automated decisions. We have 5 years of experience and more than 50 successful projects in the financial sector, guaranteeing compliance and accuracy.

What's Included in the Deliverables

• Analytical report with data source descriptions, feature engineering, and model architecture.
• Trained and calibrated model artifact (.pkl or .onnx) with metrics on held-out set.
• REST API on FastAPI for real-time scoring, containerized in Docker.
• Documentation: model card, operations manual, API spec (OpenAPI).
• Client team training: workshop on SHAP interpretation and drift monitoring.
• Support for 3 months post-launch (consulting, bug fixes) and access to monitoring dashboard.

How We Implement ML Scoring?

1. Data analysis and feature engineering. Audit sources, clean, generate 100+ features. Use PySpark for large volumes.
2. Model selection and training. Choose algorithm (LightGBM, XGBoost, neural nets) and hyperparameters via Optuna. Validate on time-based splits.
3. Calibration and interpretation. Platt scaling or isotonic regression. SHAP reports for business and regulator.
4. Deployment and monitoring. REST API on FastAPI, Docker containerization, PSI and Gini monitoring.
5. Documentation and compliance. Report for Central Bank, model description, team training.

Practical Case Study

Our client—a microfinance organization. Initial model: logistic regression with 18 features from credit bureau. Gini coefficient = 0.52 on test set.

We performed:

• Added 140 transaction features (spending patterns, cash-in/out ratio, regularity).
• LightGBM with hyperparameter tuning via Optuna (300 trials).
• Feature selection: kept top-80 features by SHAP importance.
• Calibration: Platt scaling for real PD.
• Result: Gini coefficient on test set—0.71 (+19 p.p.). Approval rate at same default level increased by 12% (approving more good borrowers). The approval boost generated additional revenue of 15 million RUB per year, with savings of up to 5 million RUB annually.

Timeline: 8–12 weeks for a basic ML model, 4–6 months for production system with monitoring, interpretability, and compliance. Contact us for a consultation to assess your project. Order a pilot project—get a ready model in 2 weeks. Reach out to us for implementing ML scoring in your bank.