Imam Sadiq University

Fraudulent activities within banking transactions pose a significant challenge for the banking sector, occurring either individually or as part of an organized scheme. It is always difficult to identify such illegal activities. Despite the development of various models and algorithms to tackle this issue, the intricate and diverse nature of fraud patterns presents difficulties in detecting all suspicious transactions. Researchers have suggested using graph theory to consider the interactions between transactions in order to overcome this challenge. Another challenge is the increased false positive error when investigating individual transactions that exhibit behavior similar to high-risk behavior. To improve the understanding of the transaction process, the use of sequence-based approaches has been proposed. In this article, a model that combines graph and sequence theory was developed to detect organized fraud. The first phase of the model involved extracting network features from the transaction graph and applying a hidden Markov chain to capture the sequential nature of the transactions. In the second phase, fraud detection was performed using a combination of the support vector machine and the improved honey badger metaheuristic algorithm. This algorithm aims to enhance fraud detection efficiency by adjusting the parameters of the support vector machine. The proposed model was evaluated on three datasets—two real-world datasets and one benchmark dataset. Performance was assessed using precision, accuracy, recall, F1-score, ROC-AUC, and PR-AUC metrics. On average, the method achieved an F1-score of 92

‌Context & Objective: The expanding role of payment service providers in the global financial ecosystem and the increasing reliance of digital transactions on their infrastructure necessitate a rigorous review of regulatory policies. Over a decade ago, the Central Bank of Iran restricted the payment market to twelve companies, effectively halting the issuance of new licenses. This policy created a static market structure, prompting a formal complaint to the Iranian Competition Council, which initially ruled the moratorium as anti-competitive. However, the Board of Appeal overturned this decision, raising fundamental questions about market regulation. The objective of this research is to evaluate the legal validity of the Central Bank of Iran's cessation of licensing for payment service providers. Specifically, the central research question investigates how this licensing moratorium and the subsequent appellate ruling are interpreted under the competition rules within the Iranian legal system. ‌Method & Approach: This research is conducted using a doctrinal methodology integrated with a legal-institutional analysis model. It systematically examines the legal, economic, and regulatory dimensions of the Central Bank's licensing moratorium. The study analyzes the administrative decisions and the Board of Appeal's ruling strictly within the framework of domestic competition laws, statutory anti-monopoly provisions, and the constitutional mandates governing the national market. ‌Findings: The analysis reveals that the Central Bank's prolonged refusal to issue new licenses constitutes a violation of statutory duties, particularly Article 7 of the Law on the Implementation of the General Policies of Article Forty-Four of the Constitution of Iran, which expressly prohibits denying business licenses under the pretext of market saturation. Furthermore, the regulatory mandate stipulating that banking institutions must hold a minimum of fifty-one percent of the shares in any payment service provider restricts the market entry of independent firms and institutionalizes a discriminatory advantage for established credit institutions. The evaluation of the Board of Appeal's ruling demonstrates fundamental flaws. Procedurally, the board accepted the Central Bank's appeal well beyond the mandatory twenty-day statutory deadline. Substantively, the board failed to address the Central Bank's direct contravention of binding Cabinet approvals aimed at eliminating monopolies, improperly justifying the restrictive practices by citing broad prudential regulatory concerns. ‌Conclusion: The monopoly engineered by the Central Bank's licensing moratorium lacks valid legal justification and practically results in market obstruction, the entrenchment of existing corporate dominance, and the perpetuation of anti-competitive behavior. The Board of Appeal's decision to overturn the Competition Council's initial ruling is devoid of sufficient legal foundation. To genuinely enforce anti-monopoly legal requirements and restore healthy competition within the national payment ecosystem, it is necessary to amend the prevailing executive regulations. Specifically, the regulatory framework must be reformed to eliminate the mandatory fifty-one percent bank ownership requirement. Abolishing this structural barrier will facilitate the participation of independent investors and align the Central Bank's regulatory architecture with its statutory obligations.

This article operationalizes Islamic rapprochement as a governed, measurable program. It fuses Causal Layered Analysis litany, systems, discourse, myth with a KPI-driven performance architecture (UCTA-PA) comprising 280 indicators: 140 Crisis Warning Indicators (CWIs) and 140 Strategic Progress Indicators (SPIs). A mixed-methods design integrates AI-assisted corpus and network analytics, purposive–stratified expert input, institutional document analysis, and pre-registered quasi-experimental estimators (staggered difference-in-differences, synthetic control, interrupted time-series). Programmatic validation shows target-concordant movements: reductions in Hate-Speech Rate and Polarization Velocity; increases in the Theological Respect Index and Joint-Institution Density; improvements in legal Interoperability Score; and severity-weighted declines in Inter-sect Incident Rate. Mechanism checks indicate discourse improvements precede incident reductions under high implementation fidelity. Theoretically, the study extends CLA by specifying a governance-grade indicator grammar that projects deep discursive and mythic drivers onto observable signals. Practically, it delivers a thermostat for decision-makers traffic-light thresholds tied to corrective playbooks, explicit ownership, and cadences enabling auditable progress toward rapprochement within a 12–24-month horizon. Actionable recommendations include publishing an indicator dictionary with non-compensatory floors for CWIs, aligning financing and recognition to verified SPI gains, and embedding narrative telemetry in data-governance pipelines with bias auditing and privacy-by-design.

The main objective of this research is to explain and design a comprehensive framework for the civil liability of matchmaking platforms based on Artificial Intelligence (AI) and with concentration on vacuums that caused by black box algorithm and showing the way to the restitution of the users. This research is done by descriptive-analytical approach and by using the library resources. The research findings show that despite the traditional approaches that know this platform as an intermediate, the findings of research show that in Imami jurisprudence, focusing on rules such as guarantee, attribution, deception, and waste, as well as in Iranian law. The comparative study also indicates that the risk-based and protection-oriented approach of the European Union, due to its alignment with the principles of justice in Iranian jurisprudence and law, is more adaptable to the legal needs of Iranian society. Innovation of research proves the present mechanisms of electronic trade law is inadequate to covering the algorithmic errors and should be accepted “the right on explain” and “audit algorithm” such former obligations to obtaining the civil liability in precedent the responsibility of the operators of these platforms can be considered both fault-based and strict liability in nature in proving of these mechanism

Purpose This study aims to introduce a theoretical blueprint for balanced data practices – the Islamic Data Ethics Theory (IDET) – to address ethical gaps in data-driven marketing. It translates Islamic moral principles into actionable guidelines ensuring integrity, fairness and accountability in digital business environments. Design/methodology/approach A qualitative, exploratory design grounded in Islamic textual analysis was used. Qur’anic verses and Hadiths were systematically screened for authenticity and relevance, then thematically coded into 34 basics, 22 organizing and 9 overarching ethical principles, ensuring methodological rigor and theological validity. Findings Nine principles were identified – Autonomy, Dignity, Justice, Commitment, Benevolence, Trust, Caution, Honesty and Noninvasiveness. Among these, Trust, Caution and Dignity are uniquely Islamic, providing distinct guidance for responsible data governance. IDET operationalizes these into moral standards for transparent consent, equitable data use and user-centric privacy mechanisms. Research limitations/implications As a theoretical study based on interpretive textual analysis, IDET requires interdisciplinary and empirical validation across industries to assess its practical effectiveness. Practical implications IDET guides organizations in creating transparent consent systems, preventing data misuse, ensuring algorithmic fairness and aligning business operations with Islamic ethical and corporate social responsibility principles to enhance trust and social equity. Originality/value IDET extends global data ethics frameworks by grounding them in divine accountability (niyyah) and communal welfare (maslahah). It offers policymakers, businesses and scholars a faith-informed, context-sensitive framework integrating spirituality, morality and governance – addressing critical gaps in secular models.