In today’s fast changing digital world, the need for secure, transparent, and reliable financial transactions is more important than ever especially in areas where fraud, delays, and unauthorized access are common concerns. Traditional payment systems often depend on centralized middlemen, which can lead to slow processing, high fees, and risks of data tampering or cyberattacks. This work introduces an automated payment processing system powered by blockchain technology, designed to make digital transactions faster, safer, and more trustworthy without relying on third parties. The motivation for this system came from real-world frustrations with issues like payment fraud, slow transactions, and the lack of visibility in how money moves within traditional financial systems. To build this system effectively, the Structured Systems Analysis and Design Methodology (SSADM) was adopted. This method provides a clear, step-bystep approach for understanding problems and creating effective systems. With blockchain at its core, the system will support real-time transaction validation, ensure that data can’t be altered, reduce costs, and remove central points of failure. Overall, it aims to build user confidence and create a more resilient payment infrastructure. By solving key problems found in conventional systems, this project hopes to contribute to the next generation of secure, scalable, and efficient financial technologies for businesses and organizations.
The study aimed to investigate the perceived application of Blockchain technology among accountants, auditors, bankers, and other related professionals in Iraq and the statistical association between this perceived application and financial-information reliability, based on respondents’ perceptions of financial-information reliability. The study was designed as a field study using a five-point Likert scale. The analysis was based on 150 valid responses. Blockchain application was measured using ten items, and financial information reliability was measured using another ten items. Cronbach's alpha coefficient, descriptive statistics, Pearson and Spearman correlation coefficients, and simple linear regression were used. The results of the Blockchain scale showed acceptable internal consistency (α = 0.775), while the financial information reliability scale showed very high internal consistency (α = 0.989). The mean scores were 4.232 and 4.221, respectively. Pearson's correlation coefficient was positive but not statistically significant (r = 0.146, p = 0.076), and the regression model was also not statistically significant (R² = 0.021, F(1, 148) = 3.203, p = 0.076). The results indicate positive perceptions of Blockchain technology and the reliability of financial information. However, the current data do not provide sufficient evidence at the 5% significance level that perceived Blockchain application is statistically significantly associated with financial-information reliability.
This study introduces an FKF-enabled intelligent supply-chain framework that integrates Artificial Intelligence (AI), Blockchain, Internet of Things (IoT), Digital Twins, and quantum optimization into a unified architecture. The FKF transform provides a mathematical spectral representation of supply-chain signals, enabling the identification of temporal shifts, modulation effects, multiscale patterns, demand fluctuations, and lead-time dynamics. These spectral features can be supplied to AI and machine-learning models to improve forecasting, anomaly detection, disruption prediction, and resilience assessment. IoT devices continuously provide real-time operational data from transportation, inventory, production, and logistics processes, while Blockchain supports secure data sharing, traceability, and transaction transparency across supply-chain participants. Digital Twins complement these technologies by creating dynamic virtual representations of physical supply-chain systems, allowing alternative scenarios, disruptions, and recovery strategies to be simulated before implementation. Quantum annealing is incorporated to address selected computationally intensive combinatorial decisions, such as routing, scheduling, resource allocation, and logistics configuration. By connecting FKF-based mathematical spectral intelligence with AI-driven analytics, trusted digital infrastructure, simulation capabilities, and emerging quantum optimization, the proposed framework provides an integrated pathway toward more predictive, adaptive, transparent, sustainable, and resilient supply-chain management. The content should be logically organized in a single paragraph, maintaining coherence and clarity throughout. Ensure that the abstract captures the research context, problem statement, approach, key results, and final conclusions in a balanced manner. Keywords— Quantum Computing; Quantum Annealing; Logistics Optimization; Unit Load Device Configuration; Supply Chain Management; Artificial Intelligence; Digital Twins; Blockchain; Supply Chain Resilience; FKF Transform.