Conceptual Model of the Decision-making System Based on Fuzzy Clustering and Nash Equilibrium: Application to the Financial Authorities of the DRC

Jean Cibamba Kanyinda; Bruce Mbombi Bakondolo; Daniel Aluba Mulumba; Richard Kitondua Lubanzadio

doi:10.36948/ijfmr.2026.v08i05.83127

Conceptual Model of the Decision-making System Based on Fuzzy Clustering and Nash Equilibrium: Application to the Financial Authorities of the DRC

Author(s)	Dr. Jean Cibamba Kanyinda, Dr. Bruce Mbombi Bakondolo, Dr. Daniel Aluba Mulumba, Professeur Dr. Richard Kitondua Lubanzadio
Country	Congo (Democratic)
Abstract	This article proposes a conceptual and strategic overhaul of the decision-making system of the financial authorities of the Democratic Republic of Congo (DGI, DGDA, and DGRAD) within a context marked by the fragmentation of tax data, the inefficiency of traditional control mechanisms, information asymmetries, and difficulties in institutional coordination. Addressing the limitations of classical approaches to financial governance, this research develops a hybrid decision-making model integrating artificial intelligence and game theory techniques to improve the performance of public tax administrations. Fuzzy C- Means (FCM) clustering algorithm and Nash equilibrium to design an intelligent system capable of segmenting taxpayers in an environment characterized by uncertainty, imprecision, and variability in tax behavior. Fuzzy clustering allows taxpayers to be classified according to multiple degrees of membership, thus offering a more realistic representation of tax risk profiles and compliance levels. In parallel, Nash equilibrium is used to model the strategic interactions between different tax authorities in order to optimize mechanisms for cooperation, information sharing, and collective decision-making. The methodology relies on a conceptual model of the system, a mathematical formalization of the segmentation and coordination mechanisms, and an analytical simulation to evaluate the performance of the proposed model. The results show that a fuzzy risk-based approach significantly improves the identification of high-risk taxpayers, reduces tax harassment related to redundant audits, and promotes a better allocation of administrative resources. Furthermore, the integration of the Nash equilibrium allows for the transformation of situations of institutional competition into cooperative strategies that maximize social surplus, decision-making efficiency, and public revenue. This research represents a scientific contribution at the intersection of artificial intelligence, intelligent decision-making systems, game theory, and digital financial governance. It also opens up important perspectives for modernizing tax administrations in emerging economies through the use of hybrid approaches based on AI and strategic optimization.
Keywords	Keywords : Fuzzy clustering ; Fuzzy C- Means ; Nash equilibrium; Game theory; Intelligent decision-making system; Financial authorities; Tax governance; Artificial intelligence; Tax risk analysis; Decision optimization; Big data analysis ; Democratic Republic of Congo.
Field	Ordinateur > Intelligence artificielle/Simulation/Réalité virtuelle
Published In	Volume 8, Issue 5, September-October 2026
Published On	2026-09-27
DOI	https://doi.org/10.36948/ijfmr.2026.v08i05.83127

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Conceptual Model of the Decision-making System Based on Fuzzy Clustering and Nash Equilibrium: Application to the Financial Authorities of the DRC

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