Safeguarding Financial Integrity with Interval-Valued Neutrosophic Analytic Hierarchy Process for Sustainable Accounting Systems

Abstract

Nowadays, financial integrity within sustainable accounting systems is critical endeavor in ensuring intricate landscape of sustainable finance. Detection of financial fraud within sustainable accounting systems is crucial for upholding environmental, social, and governance (ESG) standards and sustaining the integrity of financial practices. Leveraging advanced AI-driven technologies, these systems can effectively analyze abundance of financial data to detect suspicious patterns and anomalies indicative of fraudulent activities. Incorporating Neutrosophic logic into sustainable accounting systems improves the efficiency of financial fraud detection by accommodating inherent uncertainty in complex financial data. By leveraging this ground-breaking technology, organizations can effectively navigate the complex financial landscape while ensuring the integrity of their accounting practices. Neutrosophic logic facilitates the modelling of contradictory and ambiguous information, enabling more nuanced detection and analysis of fraudulent activities that may remain unnoticed. This study develops an automated financial fraud detection using improved sparrow search algorithm with Interval-Valued Neutrosophic Analytic Hierarchy Process (ISSA-IVNAHP) technique. The ISSA-IVNAHP technique aims to protect financial integrity via the identification of financial frauds in Sustainable Accounting Systems. The ISSA-IVNAHP technique incorporates a two-stage process. Initially, the ISSA-IVNAHP method designs ISSA-based feature subset selection approach for the optimal feature selection. Next, in the second stage, the ISSA-IVNAHP technique uses IVNAHP technique for decision-making process that enables to detection of the presence and absence of financial fraud. The simulation results of the ISSA-IVNAHP technique can be examined on financial fraud database. The experimental values reported that the ISSA-IVNAHP methodology attains maximum effeciency over other models