🏦 Banking Analytics Suite: Risk, Governance & Forecasting

A comprehensive Data Science toolkit for the Banking & Finance sector. > Integrates Regulatory Data Governance (BCBS 239), Advanced Time-Series Forecasting, and Stress Testing into a modular, production-grade pipeline.

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📖 Executive Summary

In the modern Banking & Finance landscape, models are only as good as the data feeding them. The Banking Analytics Suite is an end-to-end framework designed to address the "Trinity of Banking Data":

1. Reliability (Governance): Ensuring data entering the system is compliant and error-free using Great Expectations.
2. Accuracy (Forecasting): Using a Champion/Challenger tournament to automatically select the best predictive model (Prophet vs. ARIMA vs. Holt-Winters).
3. Resilience (Stress Testing): Simulating "Black Swan" market shocks to test liquidity and capital adequacy.

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🏗️ Technical Architecture

The pipeline follows a strict ELT (Extract, Load, Transform) pattern with an embedded Quality Gate.

graph LR
    subgraph Ingestion Layer
    A["Market Data API<br/>(Yahoo Finance)"] --> B["Raw Data Staging"]
    end
    
    subgraph Governance Layer
    B --> C{"Quality Gate<br/>(Great Expectations)"}
    C -- "Fail (Violation)" --> D["Alert: Pipeline Halted"]
    C -- "Pass (Valid)" --> E["Clean Data Lake"]
    end
    
    subgraph Modeling Layer
    E --> F["Model Tournament"]
    F --> G["Prophet (Champion)"]
    F --> H["ARIMA (Challenger)"]
    F --> I["Holt-Winters (Baseline)"]
    end
    
    subgraph Application Layer
    G --> J["Evaluator (RMSE)"]
    J --> K["Streamlit Dashboard"]
    K --> L["Stress Test Simulation"]
    end

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