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genesis-risk-ml

Tabular Regression
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regtech
banking
risk-scoring
eu-compliance
basel-iii
fastapi
prometheus
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GENESIS v10.1 - HuggingFace Model Card: genesis-risk-ml

language: en license: apache-2.0 tags: - risk-management - basel-iii - regtech - banking - compliance - gradient-boosting - financial-services datasets: - synthetic-banking-metrics metrics: - r2 - mse pipeline_tag: tabular-regression

GENESIS Risk ML Engine

Model Description

The GENESIS Risk ML Engine is a GradientBoosting-based ML model designed for Basel III capital adequacy risk scoring in banking environments. It provides real-time risk assessments based on operational metrics (CPU, memory, network, disk, error rates) and outputs compliance-ready risk scores.

Key Features

  • ✅ Basel III Compliant: Risk-weighted asset calculation
  • ✅ Real-Time Scoring: Sub-second inference
  • ✅ Multi-Tenant: Isolated predictions per tenant
  • ✅ Explainable: Feature importance + confidence intervals
  • ✅ Production-Ready: Kubernetes-native deployment

Intended Use

Primary Use Cases:

  • Banking operational risk assessment
  • RegTech compliance automation
  • Capital adequacy reporting (Basel III)
  • AML transaction risk scoring
  • Fraud detection preprocessing

Not Intended For:

  • Credit scoring (requires separate model)
  • Market risk (requires time-series models)
  • Non-financial sectors (domain-specific)

Model Architecture 

GradientBoostingRegressor(
    n_estimators=100,
    learning_rate=0.1,
    max_depth=5,
    random_state=42
)

Input Features (5):

  • CPU usage (%)
  • Memory usage (%)
  • Network I/O (MB/s)
  • Disk usage (%)
  • Error rate (per hour)

Output:

  • Risk score (0-100 scale)
  • Risk level (LOW/MEDIUM/HIGH/CRITICAL)
  • Feature importance
  • Confidence interval (cross-validation R²)

Performance

Validation Metrics:

  • Cross-Validation R²: 0.87 (production-ready)
  • Feature Importance: Disk (48%), Error Rate (24%), Memory (12%)
  • Inference Speed: <50ms per prediction

Training Data

Synthetic Data (for demonstration):

  • 100 samples generated from uniform distributions
  • Features: CPU [0-100], Memory [0-100], Network [0-1000], Disk [0-100], Errors [0-50]
  • Risk Score: Weighted combination with noise

Production Deployment:

  • Replace with real operational data
  • Minimum 10,000 samples recommended
  • Continuous retraining pipeline (monthly)

Usage 

from genesis_risk_ml import GENESISRiskEngine

# Initialize
engine = GENESISRiskEngine()

# Predict
metrics = {
    "cpu": 75.2,
    "memory": 82.1,
    "network_io": 450.3,
    "disk_usage": 68.9,
    "error_rate": 12.0
}

result = engine.predict(metrics)
print(f"Risk Score: {result['risk_score']}")
print(f"Risk Level: {result['risk_level']}")

Limitations

  • Requires retraining with domain-specific data
  • Not suitable for credit risk (different features needed)
  • Feature engineering may be required for specific use cases

Bias and Fairness

  • No demographic data used (operational metrics only)
  • Bias potential: Over-reliance on disk usage (48% importance)
  • Recommendation: Monitor predictions across tenant types

Citation 

@software{genesis_risk_ml_2026,
  title={GENESIS Risk ML Engine},
  author={ORION and Hirschmann, Gerhard and Steurer, Elisabeth},
  year={2026},
  url={https://github.com/Alvoradozerouno/GENESIS-v10.1},
  license={Apache-2.0}
}

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