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

This record documents the authorship and technical structure of the AFMF AI/ML Simulation Suite, the first simulation framework to integrate ESG risk, sovereign risk, and AI-enhanced forecasting into financial risk modeling. Developed under the Alaali Financial Models Framework (AFMF), the system applies Monte Carlo simulations (10,000 runs), Random Forest classification, and macro-financial stress tests to assess financial distress, capital structure resilience, and liquidity volatility. The included PDF summarizes the framework’s logic and strategic value, while the structured Excel file provides the simulation variable format. Full simulation outputs are withheld to protect intellectual property. This dataset complements AFMF Volume 1.0 and supports academic citation, validation protocols, and future licensing of proprietary models including A-ICR (Interest Coverage Resilience), A-CFVI (Cash Flow Volatility Index), and ESG-WACC.本记录详细记述了AFMF AI/ML仿真套件的作者信息与技术架构。该套件是首个将环境、社会和治理（Environmental, Social and Governance, ESG）风险、主权风险以及人工智能增强型预测集成至金融风险建模领域的仿真框架。本套件基于阿拉利金融模型框架（Alaali Financial Models Framework，简称AFMF）开发，采用蒙特卡洛模拟（10000次运行）、随机森林分类以及宏观金融压力测试，用以评估财务困境、资本结构韧性与流动性波动状况。随附的PDF文件概述了该框架的逻辑内涵与战略价值，而结构化Excel文件则提供了仿真变量的格式规范。为保护知识产权，完整的仿真输出未予公开。本数据集作为AFMF 1.0卷的补充资料，可用于学术引用、验证规程，以及未来包括A-ICR（利息保障韧性，Interest Coverage Resilience）、A-CFVI（现金流波动指数，Cash Flow Volatility Index）与ESG-WACC在内的专有模型的授权许可。