AFMF AI/ML Simulations: Authorship Record & Monte Carlo Scenario Results

This document formally declares the authorship, structure, and application of the AI- and ML-powered simulation suite developed under the Alaali Financial Models Framework (AFMF). This simulation system is the first of its kind to integrate:

Sovereign Support Adjustment Factor (SSAF)

ESG-adjusted cost of capital (A-ESG-WACC)

Advanced interest coverage diagnostics (A-ICR)

Cash flow volatility analytics (A-CFVI)

AI/ML forecasting engines using Monte Carlo simulations, Random Forest, and Gradient Boosting

The AI/ML simulation engine was deployed to run 10,000 scenario-based stress tests on real-world case studies, beginning with Aluminium Bahrain (Alba) and Alcoa, integrating geopolitical risk, liquidity volatility, and ESG sensitivity into probabilistic forecasting.

This Zenodo upload provides a timestamped, citable record of the technical innovation, first authorship, and proof of execution, including:

A descriptive PDF of the simulation logic and framework

An Excel file with 10,000 simulation outputs (Monte Carlo runs)

Documentation for future licensing and empirical validation