AI-Enabled Management Control and Treasury Management for Financial Resilience, A Financial-Agility Framework, Econometric Research Design, and Reproducible Simulation

Abstract

Artificial intelligence may improve treasury forecasts without necessarily improving an enterprise’s capacity to withstand financial disruption. This paper develops an implementation-ready research method connecting artificial intelligence capability, management control, treasury quality, financial agility, and financial resilience. A structured, multi-source literature synthesis motivates a governed architecture combining accounting-data reconciliation, probabilistic cash-flow forecasting, constrained liquidity decisions, and human authorization. The proposed Moroccan field study links a prospective enterprise panel to transaction records and repeated multiformat surveys. A randomized phased offer is preferred; cohort-specific difference-in-differences provides a conditional observational alternative. Dynamic panel models, nonlinear adjustment, and longitudinal mechanism analysis address distinct secondary questions rather than substituting for identification. Because no enterprise dataset was supplied, the numerical evidence consists exclusively of an executed Monte Carlo experiment: nine scenarios, 500 replications each, and four estimators. In the reference scenario with 300 synthetic firms and eight periods, nonlinear fixed effects and firm-block cross-fit series estimation recover an imposed coefficient of 0.18, whereas pooled and mis specified linear fixed-effects regressions remain biased. Cross-fit interval coverage is 93.8%; an omitted time-varying confounder raises bias to 0.241 and destroys coverage. For an imposed smaller effect of 0.06, simulated power increases from 44.2% with 150 firms to 96.8% with 600 firms. These findings validate limited aspects of the statistical workflow, not the business effectiveness of artificial intelligence. The contribution is a falsifiable, auditable protocol integrating forecast evaluation, causal inference, managerial mechanisms, and operational safeguards.

Keywords: management control; treasury management; artificial intelligence; financial resilience; financial agility; panel econometrics; Morocco.

Received Date: August 17, 2026

Accepted Date: September 09, 2026

Published Date: October 01, 2026

Available Online at: https://www.ijsrisjournal.com/index.php/ojsfiles/article/view/1014