The challenge of unregulated credit creation and the diversion of facility consumption in underbanked regions has persistently undermined the effectiveness of distributive justice policies in Iran's banking system. Despite various policy interventions over the past decade, the lack of systematic post-disbursement supervision mechanisms has led to continuous imbalances, with liquidity growth reaching 24.3 percent in 2023 and non-performing loans increasing from approximately 420 thousand billion tomans in 2019 to about 780 thousand billion tomans in 2023. This situation indicates that credit resources are not effectively directed toward productive activities, which from the perspective of Islamic transactional jurisprudence constitutes a violation of the prohibitions against uncertainty (gharar), unjust appropriation of wealth (akl māl bi-al-bāṭil), and the obligation to fulfill contractual commitments (wafāʾ bi-al-ʿahd). The persistence of facility disbursement without effective mechanisms to ensure consumption aligns with agreed-upon objectives transforms the credit relationship from a legitimate commutative obligation into a process of resource transfer lacking contractual substance.

Introduction

This study aims to propose and analytically validate a locally developed, justice-oriented smart supervision model for post-disbursement credit management that integrates technical efficiency with Islamic transactional jurisprudence requirements within the institutional context of Iran's banking system. It also attempts to identify operational data-driven mechanisms capable of detecting and controlling credit diversion in near real-time, and to demonstrate how Islamic transactional jurisprudence rules can be transformed from normative principles into practical, implementable indicators within banking supervision processes.

Methods and Materials

A mixed-methods exploratory research design was employed. The statistical population consisted of experts in banking, monetary policy, and Islamic transactional jurisprudence. Theoretical saturation was approached after sixteen interviews; sampling was extended to twenty participants to strengthen coverage of technical, institutional, and jurisprudential dimensions. Data collection instruments included a semi-structured interview (researcher-made, 2023) in the qualitative phase and a fuzzy Delphi questionnaire in the quantitative phase. The qualitative data were analyzed using three-step thematic analysis consisting of open coding, axial coding, and selective coding. The quantitative phase employed the fuzzy Delphi technique with calculation of the fuzzy consensus index (λ), where only components achieving λ ≥ 0.7 were retained in the final model. Validity was established through expert review, and reliability was confirmed through response stability assessment in the final Delphi round. An analytical-scenario evaluation was also conducted using aggregated facility data and supervisory indicators extracted from official Central Bank reports to examine the internal consistency of the model and analyze the direction and magnitude of component effects, without claiming quantitative prediction or empirical generalization.

Findings

The findings indicated that three mechanisms achieved the required fuzzy consensus index. The first mechanism, allocation of credit to purpose-specific accounts, received a consensus score of 0.82 and was identified as the most strongly endorsed component. This mechanism prevents premature liquidity circulation by restricting the use of credit resources to pre-specified accounts and expenditure patterns. The second mechanism, phased fund release based on the Economic Credit Effectiveness Index, achieved a consensus score of 0.78. This index measures the impact of credit on the real economy by distinguishing productive fund flows from non-productive or speculative transactions. The third mechanism, transaction graph analysis with real-time anomaly detection, achieved a consensus score of 0.75. This mechanism enables ongoing transaction graph analysis to identify emerging deviations in near real-time and trigger early warning alerts. Two proposed components, i.e., machine learning-based prediction of diversion and connection to national credit databases, did not achieve the required consensus threshold, receiving scores of 0.68 and 0.66 respectively, primarily due to expert concerns regarding institutional feasibility, data accessibility, and privacy considerations. The three accepted mechanisms demonstrated significant alignment with the three core rules of Islamic transactional jurisprudence: fulfillment of contracts (wafāʾ bi-al-ʿahd) through continuous monitoring of compliance with agreed expenditure patterns, avoidance of uncertainty (gharar) through purpose-specific accounts and phased release conditional on verification of prior stage completion, and prohibition of unjust appropriation (akl māl bi-al-bāṭil) through the Economic Credit Effectiveness Index which ensures funds are released only upon realization of verifiable economic benefit.

Conclusion

It was concluded that the proposed framework integrates data-driven supervisory efficiency with jurisprudential legitimacy, providing analytical capacity to reduce credit diversion from approximately baseline levels to significantly lower rates, as demonstrated by scenario-based simulations using aggregated supervisory evidence. The framework serves as a complementary mechanism within the banking supervision system, not as a substitute for initial credit assessment mechanisms or a guarantee of comprehensive social justice realization. The main limitation of this study is its analytical nature and reliance on aggregated data, as the scenario evaluation was conducted solely to examine direction of effects and internal consistency, not to provide predictive accuracy or empirical generalizability. Future research should include field-based empirical testing of the framework within real institutional contexts. The contribution of this paper lies not in presenting an operational or prescriptive formula, but in redefining justice as an institutionally measurable and analyzable concept within the banking system and advancing the analytical frameworks related to post-disbursement supervision.