Agilance: An intelligent strategic control and financial planning system for data-driven environments
TL;DR
The study contributes an auditable AI-supported framework for explainable strategic financial planning in data-intensive organizational environments and identifies the need for future validation using anonymized multi-organizational datasets, externally audited protocols, or independently reproducible benchmarks.
Abstract
Type of the article: Research ArticleThis study proposes Agilance as a conceptual and technical framework for explainable strategic financial planning in data-intensive organizational environments. The framework is based on a custom transformer architecture that incorporates three sector-specific components: Financial Relevance Weighting, Context Shift Stabilization, and Output Compression. Because the evaluation was conducted on a confidential sector-specific dataset and through internal benchmarking procedures, the underlying source data and job-level operational records cannot be publicly released. Within these constraints, the internal evaluation yielded indicative results: 96.03% accuracy and 95.8% F1-score for priority classification on the held-out test set, 90.26% accuracy and 90.22% F1-score for implementation-duration classification, and an average 10-fold cross-validation accuracy of 91.7%. The expert explainability assessment produced mean scores of 4.67 for clarity, 4.53 for trustworthiness, and 4.48 for actionability, with inter-rater agreement ranging from 0.87 to 0.91. Internal operational benchmarks further suggested planning-cycle reductions and economic benefits, including 95.8% improvement in real-time data analysis and time-zone synchronization, 5.4% operational cost savings, and an illustrative first-year ROI of 46%. These results should be interpreted as preliminary internal evidence obtained under specific evaluation conditions, not as independently verified proof of broad organizational generalizability. The study contributes an auditable AI-supported framework and identifies the need for future validation using anonymized multi-organizational datasets, externally audited protocols, or independently reproducible benchmarks.Acknowledgments The publication fees of this manuscript have been financed by the MSc Tax and Financial Services Digital Transformation (DITAF), University of Patras.