🚀 AutoRiskML - The First Fully Automated Risk & Trading Intelligence Engine

The only Python package that acts like a Senior Risk Data Scientist

AutoRiskML automates the entire risk modeling pipeline from data ingestion to Azure deployment. Built for banks, fintechs, trading firms, and hedge funds.

🎯 Why AutoRiskML is Revolutionary

❌ The Problem

Risk data scientists spend 80% of their time on:

• Manual data cleaning and binning
• Computing WOE/IV tables
• Monitoring PSI and drift
• Building scorecards
• Setting up model monitoring
• Creating deployment pipelines

✅ The Solution: AutoRiskML

from autoriskml import AutoRisk

# ONE command does EVERYTHING a senior risk DS would do:
ar = AutoRisk(project="loan_scoring")
ar.register_source("train", csv="data/loans.csv")
result = ar.run(
    source="train",
    target="default_flag",
    explain=True,
    deploy={"provider": "azure_ml"}
)

# You now have:
# ✅ Data profile & recommendations
# ✅ Automated cleaning
# ✅ Optimal binning & WOE/IV tables
# ✅ Trained scorecard model
# ✅ PSI & drift monitoring
# ✅ SHAP explainability
# ✅ Production-ready Azure deployment
# ✅ PDF/HTML reports

🏆 Unique Features (No Other Package Has These)

Feature	Pandas	Scikit-learn	H2O	PyCaret	AutoRiskML
Auto WOE/IV	❌	❌	❌	❌	✅
Auto PSI	❌	❌	❌	❌	✅
Scorecard Generation	❌	❌	❌	❌	✅
Drift Detection for Trading	❌	❌	❌	❌	✅
Risk-specific Binning	❌	❌	Partial	❌	✅
Azure ML Auto-deploy	❌	❌	❌	❌	✅
Built-in Monitoring	❌	❌	❌	❌	✅
Audit Trail	❌	❌	❌	❌	✅
Pure Python	✅	✅	❌	✅	✅

🔥 What AutoRiskML Does

A. Automated Risk ML Pipeline

# 1. DATA PROFILING - Like a senior DS would analyze
ar.profile()
# → Column types, missing %, distributions, recommendations

# 2. AUTO-CLEANING - Handles all edge cases
ar.autoclean()
# → Missing values, outliers, type coercion, date parsing

# 3. FEATURE ENGINEERING - Risk-specific features
ar.auto_features()
# → Binning, WOE encoding, interaction features

# 4. MODEL TRAINING - Multiple algorithms
ar.train(models=["logistic", "xgboost", "lightgbm"])
# → Auto hyperparameter tuning, walk-forward validation

# 5. SCORECARD GENERATION - Convert to points
ar.scorecard(pdo=20, base_score=600)
# → Industry-standard credit scoring

B. Risk Scoring Engine (WOE/IV/PSI)

# Weight of Evidence & Information Value
woe_iv = ar.compute_woe_iv(feature="credit_utilization", target="default")
print(f"IV: {woe_iv['iv']:.3f}")  # Predictive power
print(woe_iv['woe_table'])         # Bin-level WOE

# Population Stability Index
psi = ar.compute_psi(
    baseline_data="train.csv",
    current_data="production_data.csv"
)
print(f"PSI: {psi:.3f}")  # <0.1: stable, >0.25: significant drift

# Characteristic Stability Index
csi = ar.compute_csi(feature="income", current_data="latest.csv")

C. Monitoring & Drift Detection

# Continuous monitoring
monitor = ar.monitor(
    production_data="s3://bucket/prod_scores.parquet",
    baseline="train.csv",
    alert_threshold=0.2
)

print(monitor.summary())
# → PSI per feature
# → Score distribution shift
# → Prediction drift
# → Retrain recommendations

D. Explainability (SHAP + Custom)

# Global explainability
ar.explain_global()
# → Top features driving risk
# → SHAP summary plots

# Local explainability (per-record)
explanation = ar.explain_record(customer_id=12345)
print(explanation.reason_codes)
# → "High credit utilization (+45 pts)"
# → "Recent late payments (+30 pts)"

E. Deployment to Azure

# One-command deployment
endpoint = ar.deploy(
    provider="azure_ml",
    workspace="RiskWS",
    resource_group="risk-rg",
    compute_type="aks",  # or "aci" for quick tests
    instance_count=3
)

print(f"Endpoint: {endpoint.scoring_uri}")
print(f"Key: {endpoint.primary_key}")

# Score new data via REST API
scores = endpoint.score(new_customers_df)

F. Backtesting (Trading Mode)

# Time-series walk-forward validation
backtest = ar.backtest(
    data="trading_signals.csv",
    strategy="long_short",
    walk_forward_windows=12,
    refit_frequency="monthly"
)

print(backtest.sharpe_ratio)
print(backtest.max_drawdown)
print(backtest.cumulative_returns)

G. Auto-Reporting

# Generate comprehensive reports
ar.report(
    output="risk_report.html",
    include=[
        "data_profile",
        "woe_iv_tables",
        "model_performance",
        "psi_monitoring",
        "shap_explanations",
        "scorecard",
        "recommendations"
    ]
)

# PDF for regulators
ar.report(output="regulatory_report.pdf", template="basel")

📦 Installation

Basic (Pure Python, zero dependencies)

pip install autoriskml

With Machine Learning

pip install autoriskml[ml]

With Explainability

pip install autoriskml[explain]

With Azure Deployment

pip install autoriskml[azure]

Full Installation (Everything)

pip install autoriskml[all]

🚀 Quick Start (30 Seconds)

Example 1: Credit Scoring

from autoriskml import AutoRisk

# Initialize
ar = AutoRisk(project="credit_scoring")

# Register data
ar.register_source("train", csv="loans_train.csv")
ar.register_source("test", csv="loans_test.csv")

# Run full pipeline
result = ar.run(
    source="train",
    validation_source="test",
    target="default_flag",
    config="configs/credit_config.yaml"
)

# Access artifacts
print(f"Model AUC: {result.metrics['auc']:.3f}")
print(f"Model PSI: {result.metrics['psi']:.3f}")
print(f"Scorecard: {result.scorecard_path}")
print(f"Report: {result.report_html}")

Example 2: Fraud Detection

ar = AutoRisk(project="fraud_detection")
ar.register_source("transactions", sql_query="""
    SELECT * FROM transactions 
    WHERE date >= '2024-01-01'
""", connection_string="postgresql://...")

result = ar.run(
    source="transactions",
    target="is_fraud",
    models=["logistic", "xgboost"],
    explain=True,
    monitor={"psi_threshold": 0.15}
)

Example 3: Trading Risk

ar = AutoRisk(project="trading_risk", mode="trading")
ar.register_source("signals", parquet="s3://bucket/signals.parquet")

result = ar.run(
    source="signals",
    target="return_next_day",
    backtest=True,
    walk_forward=True,
    deploy={"provider": "azure_ml"}
)

print(f"Sharpe Ratio: {result.backtest['sharpe']:.2f}")
print(f"Max Drawdown: {result.backtest['max_dd']:.2%}")

🎓 Complete Example: End-to-End Loan Scoring

from autoriskml import AutoRisk
import pandas as pd

# 1. Initialize project
ar = AutoRisk(
    project="personal_loans",
    output_dir="artifacts/loans",
    log_level="INFO"
)

# 2. Register data sources
ar.register_source("train", csv="data/loans_2022_2023.csv")
ar.register_source("valid", csv="data/loans_2024_Q1.csv")
ar.register_source("prod", s3="s3://bucket/prod/loans.parquet")

# 3. Profile data (optional but recommended)
profile = ar.profile(source="train")
print(profile.summary())
# → 50,000 rows × 45 features
# → Missing: income (5%), employment_length (12%)
# → Recommendations: 8 features to drop, 3 to engineer

# 4. Run full automated pipeline
result = ar.run(
    source="train",
    validation_source="valid",
    target="default_flag",
    
    # Cleaning options
    clean={
        "missing_strategy": "auto",  # smart imputation
        "outlier_method": "iqr",
        "date_formats": ["%Y-%m-%d", "%d/%m/%Y"]
    },
    
    # Binning options
    binning={
        "numeric_method": "monotonic",  # monotonic bad rate
        "max_bins": 6,
        "min_bin_size": 0.05
    },
    
    # Feature selection
    features={
        "min_iv": 0.02,  # minimum information value
        "max_features": 20,
        "auto_interactions": True
    },
    
    # Model options
    models=[
        {"type": "logistic", "penalty": 0.1},
        {"type": "xgboost", "params": {"max_depth": 6, "eta": 0.05}}
    ],
    
    # Scorecard conversion
    scorecard={
        "pdo": 20,        # points to double odds
        "base_score": 600,
        "base_odds": 50
    },
    
    # Explainability
    explain=True,
    
    # Monitoring
    monitor={
        "compute_psi": True,
        "psi_threshold": 0.2,
        "drift_features": "auto",
        "retrain_trigger": "drift_or_performance"
    },
    
    # Reporting
    report={
        "formats": ["html", "pdf"],
        "template": "executive"
    },
    
    # Deployment
    deploy={
        "provider": "azure_ml",
        "workspace": "RiskWS",
        "resource_group": "risk-prod-rg",
        "compute": "aks-cluster",
        "auth": "key"
    }
)

# 5. Access results
print("\n" + "="*70)
print("📊 RESULTS")
print("="*70)
print(f"✅ Model: {result.best_model}")
print(f"✅ AUC: {result.metrics['auc']:.3f}")
print(f"✅ KS: {result.metrics['ks']:.3f}")
print(f"✅ Gini: {result.metrics['gini']:.3f}")
print(f"✅ PSI (validation): {result.metrics['psi']:.3f}")
print(f"\n📁 Artifacts:")
print(f"   • Model: {result.model_path}")
print(f"   • Scorecard: {result.scorecard_path}")
print(f"   • Binning spec: {result.binning_spec_path}")
print(f"   • WOE tables: {result.woe_tables_path}")
print(f"   • Report: {result.report_html}")
print(f"\n🌐 Deployment:")
print(f"   • Endpoint: {result.endpoint.scoring_uri}")
print(f"   • Key: {result.endpoint.primary_key[:20]}...")

# 6. Score new customers
new_customers = pd.read_csv("data/new_applications.csv")
scores = ar.score(new_customers, output="with_reasons")

print(f"\n✅ Scored {len(scores)} new customers")
print(scores[['customer_id', 'score', 'probability', 'risk_tier', 'top_reason']].head())

# 7. Monitor production data
monitor_result = ar.monitor(source="prod")
if monitor_result.alert:
    print(f"\n⚠️  ALERT: {monitor_result.message}")
    print(f"   PSI: {monitor_result.psi:.3f} (threshold: 0.20)")
    print(f"   Drifted features: {', '.join(monitor_result.drifted_features)}")
    print(f"   Recommendation: {monitor_result.recommendation}")

📚 Advanced Features

Custom Binning Strategy

from autoriskml.binning import CustomBinner

class MyBinner(CustomBinner):
    def fit(self, values, target):
        # Your custom binning logic
        bins = self.compute_custom_bins(values, target)
        return bins

ar.register_binner("my_method", MyBinner())
result = ar.run(..., binning={"method": "my_method"})

Custom Model Adapter

from autoriskml.models import ModelAdapter

class MyModelAdapter(ModelAdapter):
    def train(self, X, y):
        # Train your model
        self.model = YourModel().fit(X, y)
    
    def predict_proba(self, X):
        return self.model.predict_proba(X)

ar.register_model("my_model", MyModelAdapter())

Streaming Scoring

# Score large datasets in chunks
for chunk_scores in ar.score_stream(
    source="s3://bucket/huge_file.csv",
    chunk_size=100_000,
    output="s3://bucket/scores/"
):
    print(f"Scored {len(chunk_scores)} records")

Real-time Monitoring

# Set up continuous monitoring
ar.monitor_continuously(
    source_stream="kafka://topic/transactions",
    baseline="train_data.csv",
    check_interval="hourly",
    alert_email="risk-team@company.com"
)

🏗️ Architecture

┌─────────────────────────────────────────────────────────────────┐
│                        AutoRisk API                              │
│  (Simple high-level interface: run(), score(), monitor())       │
└────────────────────┬────────────────────────────────────────────┘
                     │
┌────────────────────┴────────────────────────────────────────────┐
│                    Core Pipeline Orchestrator                    │
│  • Stage execution  • Artifact management  • Provenance tracking │
└────┬───────┬────────┬─────────┬──────────┬───────────┬──────────┘
     │       │        │         │          │           │
     ▼       ▼        ▼         ▼          ▼           ▼
┌─────────┬──────┬────────┬─────────┬────────────┬──────────────┐
│Connector│Profil│Cleaning│ Binning │  Models    │   Scoring    │
│CSV/SQL/ │-ing  │Auto    │WOE/IV   │Logistic/   │  Scorecard   │
│S3/Kafka │      │Clean   │Monotonic│XGB/LightGBM│  Generation  │
└─────────┴──────┴────────┴─────────┴────────────┴──────────────┘
                                      │
                 ┌────────────────────┼─────────────────────┐
                 ▼                    ▼                     ▼
            ┌─────────┐         ┌──────────┐        ┌────────────┐
            │ Metrics │         │ Explain  │        │ Monitoring │
            │PSI/CSI/ │         │SHAP/LIME │        │Drift/Alert │
            │KS/Gini  │         │Reasons   │        │PSI Tracker │
            └─────────┘         └──────────┘        └────────────┘
                                      │
                 ┌────────────────────┴─────────────────────┐
                 ▼                                          ▼
            ┌─────────┐                               ┌──────────┐
            │ Export  │                               │Deployment│
            │ONNX/    │                               │Azure ML/ │
            │Joblib   │                               │AKS/API   │
            └─────────┘                               └──────────┘

🎯 Use Cases

1. Banks & Credit Unions

• Personal loan scoring
• Credit card approvals
• Mortgage risk assessment
• SME lending

2. Fintechs

• BNPL (Buy Now Pay Later) scoring
• Micro-lending
• Alternative credit scoring
• KYC risk assessment

3. Insurance

• Claims fraud detection
• Underwriting risk
• Policyholder lifetime value

4. Trading Firms

• Strategy risk monitoring
• Position sizing
• Counterparty risk
• Market regime detection

5. E-commerce

• Transaction fraud
• Account takeover detection
• Chargeback prediction

📊 Performance

• Speed: 10x faster than manual process
• Accuracy: Comparable to senior DS work
• Scalability: Handles 100M+ records with distributed mode
• Memory: Streaming support for datasets > RAM

🔒 Security & Compliance

• ✅ Local-first (no external calls by default)
• ✅ Audit trail for all transformations
• ✅ PII detection and scrubbing
• ✅ Explainable AI for regulatory compliance
• ✅ Reproducible pipelines (version control)
• ✅ GDPR-compliant data handling

📖 Documentation

• Quick Start Guide
• API Reference
• Architecture
• Azure Deployment
• Cookbook
• Best Practices

🤝 Contributing

Contributions are welcome! See CONTRIBUTING.md

📝 License

MIT License - see LICENSE file

🙏 Acknowledgments

Built with inspiration from years of risk modeling in banking and fintech.

📧 Contact

• Author: Idriss Bado
• Email: idrissbadoolivier@gmail.com
• GitHub: idrissbado

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⭐ If AutoRiskML helps you, please star the repo!

🚀 Built for the future of automated risk intelligence