Architecture for a Probabilistic Risk Modeling Platform

This post outlines a platform architecture designed to model the impact of a hybrid risk registry (qualitative and quantitative risks) on an oil company’s key financial KPIs like EBITDA and Cash Flow on a monthly basis. The design emphasizes modularity, auditability, and the integration of expert judgment with stochastic simulation.

1. Core Principles & Objectives

• Single Source of Truth: Establish a centralized, versioned Risk Registry for all identified risks.
• Hybrid Modeling: Natively support both quantitative risks (modeled with probability distributions) and qualitative risks (modeled with structured expert judgment).
• Financial Integration: Directly link risk events to a baseline financial plan (P&L, Cash Flow statement) to quantify impact.
• Probabilistic Output: Move beyond single-point estimates to deliver a distribution of potential outcomes (e.g., P10/P50/P90 EBITDA).
• Auditability & Reproducibility: Ensure every simulation run is traceable to a specific version of the risk registry, assumptions, and financial baseline.
• User-Centric Workflow: Provide intuitive interfaces for risk owners to provide input without needing to be simulation experts.

2. High-Level Architecture

The platform is designed as a set of modular services that interact through well-defined APIs and a shared data layer.

graph TD
    subgraph User Interfaces
        A[Risk Registry UI]
        B[Parameter Estimation UI]
        C[Reporting Dashboard]
    end

    subgraph Core Services
        D[Risk Registry Service API]
        E[Parameterization Service API]
        F[Stochastic Simulation Engine]
        G[Financial Impact Model]
        H[Aggregation & Reporting Service]
    end

    subgraph Data & Storage
        I[Risk & Parameter DB]
        J[Baseline Financials DB]
        K[Simulation Results Store]
    end

    A --> D
    B --> E
    D <--> I
    E --> I
    
    F --> G
    F --> K
    
    H --> K
    C --> H

    D -- Risk Definitions --> F
    E -- Risk Parameters --> F
    J -- Baseline Plan --> F
    
    style F fill:#f9f,stroke:#333,stroke-width:2px
    style G fill:#ccf,stroke:#333,stroke-width:2px

3. Component Breakdown

a. Risk Registry Service

• Purpose: Manages the inventory of all risks.
• Features:
  • API & UI: CRUD operations for risks.
  • Attributes: Each risk has a unique ID, name, description, category (Market, Production, etc.), owner, status (active, mitigated, closed), and type (Quantitative/Qualitative).
  • Versioning: Any change to a risk’s definition creates a new version.
• Tech: Backed by a relational database (e.g., PostgreSQL) with a REST API (e.g., Python/FastAPI or Go).

b. Parameterization & Estimation Service

• Purpose: Allows experts and risk owners to provide inputs.
• Features:
  • Quantitative Input: For a quantitative risk (e.g., “Oil Price Volatility”), the UI prompts for a probability distribution (e.g., Normal, Lognormal, Beta) and its parameters (mean, std dev, min/max). It can also support importing time series data for fitting.
  • Qualitative Input: For a qualitative risk (e.g., “Geopolitical Unrest in Region X”), the UI uses a structured matrix approach (e.g., Impact vs. Likelihood). Experts provide scores (e.g., 1-5) which are mapped to quantitative impacts behind the scenes (e.g., a “High” impact score translates to a 5% production loss with a 10% probability of occurrence).
  • Correlation Matrix: A dedicated UI for subject matter experts to define correlations between key quantitative risks (e.g., Oil Price and FX Rate).
• Tech: A web application (e.g., React) communicating with a dedicated API that stores parameters in the Risk & Parameter DB.

c. Stochastic Simulation Engine

• Purpose: The computational core of the platform.
• Workflow:
  1. Fetch Inputs: Loads the active risk registry, the latest approved parameters, the correlation matrix, and the baseline monthly financial plan for the simulation period (e.g., next 24 months).
  2. Generate Random Variates: For each of the N simulation runs (e.g., 10,000 iterations), it draws a random value for each quantitative risk from its specified distribution, respecting the correlation matrix.
  3. Trigger Events: For each run, it determines if qualitative risks “occur” based on their assigned probability.
  4. Invoke Financial Model: For each month in each simulation run, it passes the set of risk outcomes (e.g., oil_price = $75, production_loss_event = true) to the Financial Impact Model.
• Tech: A Python-based engine using libraries like NumPy for sampling, Pandas for data manipulation, and Dask or Ray for parallelizing the simulation runs.

d. Financial Impact Model

• Purpose: Translates abstract risk outcomes into concrete financial adjustments.
• Features:
  • Rule-Based Logic: A collection of functions that modify the baseline financial plan.
  • Example Rules:
    • apply_market_risk(plan, price_outcome): Adjusts the revenue line in the P&L based on the simulated oil price.
    • apply_production_risk(plan, loss_event): If a production loss event is triggered, reduces revenue, variable OPEX, and royalties accordingly.
    • apply_capex_risk(plan, overrun_outcome): Increases the CAPEX line in the Cash Flow statement based on a simulated cost overrun percentage.
• Tech: A well-tested Python library/module, designed to be stateless and easily pluggable into the simulation engine.

e. Aggregation & Reporting Service

• Purpose: Processes the raw simulation output into meaningful insights.
• Features:
  • Post-Processing: Takes the N simulated financial outcomes for each month from the Simulation Results Store.
  • Metric Calculation: Calculates key statistics for each KPI (EBITDA, CF), such as P10, P50, P90, mean, standard deviation, Value at Risk (VaR), and Expected Shortfall (CVaR).
  • Attribution Analysis: Performs sensitivity or variance decomposition to identify the top contributing risks to overall uncertainty.
• Tech: A service that can be triggered after a simulation completes, using Python or a data warehouse’s native capabilities.

f. Data & Storage

• Risk & Parameter DB (PostgreSQL): Stores the risk registry, expert-provided parameters, and correlation matrices. Normalized and versioned for auditability.
• Baseline Financials DB (SQL or File Store): Stores the approved monthly financial plan. Can be a simple database table or a version-controlled file (e.g., CSV/Parquet on S3).
• Simulation Results Store (Data Lake / Warehouse): Stores the detailed output of every simulation run (e.g., one row per run per month with all KPI values). This raw data is crucial for deep-dive analysis and future model validation. A columnar format like Parquet is ideal.

4. Workflow & Data Flow

1. Setup: Risk owners define risks in the Registry UI. Experts provide parameters via the Estimation UI. Finance uploads the baseline monthly plan.
2. Execution: An authorized user triggers a simulation run via an API call or control panel.
3. Simulation: The Engine loads all inputs, runs the Monte Carlo simulation invoking the Financial Impact Model for each step, and writes the raw results to the Results Store.
4. Aggregation: The Aggregation Service runs automatically post-simulation, calculating summary statistics and risk attributions.
5. Reporting: The Dashboard calls the Reporting Service to display the results, allowing users to view KPI distributions, tornado charts of risk drivers, and trend analysis.

5. Implementation & Tech Stack Summary

6. Governance & Model Risk Management

• Parameter Approval: Implement a four-eyes (maker/checker) workflow for approving risk parameters before they can be used in an official run.
• Model Validation: The Financial Impact Model’s logic must be independently reviewed and tested. Back-testing simulation results against actuals should be performed periodically.
• Run Immutability: Every simulation run is associated with immutable versions of all its inputs (risks, parameters, baseline plan), ensuring perfect reproducibility.
• Access Control: Role-based access ensures that only authorized personnel can define risks, approve parameters, or trigger official simulation runs.

Historical reconstruction for knowledge retention & reference.