An Islamic Juristic Framework for Data and Algorithmic Monetisation in the Digital Economy

Purpose — Islamic financial institutions increasingly rely on personal data and trained machine-learning models to perform credit scoring, customer profiling, and product recommendations. Classical jurisprudence, however, provides no settled position on whether such data and models may be lawfully owned, exchanged, or monetised. This study addresses this lacuna by constructing a structured ijtihād (juristic reasoning) procedure that derives a ḥukm (Sharīʿah ruling) on data and algorithmic monetisation, and by translating each juristic step into compliance instruments that Sharīʿah boards, financial-technology developers, and regulators already operate. Design/Methodology/Approach — The study adopts qualitative doctrinal analysis. Contested points among the four Sunni schools are resolved through tarjīḥ bi al-dalīl (preference on the strength of evidence), and the resulting method is organised into a five-step sequence: tashkhīṣ (diagnosis), takyīf fiqhī (legal characterisation), taḥqīq al-manāṭ (verification of the operative cause), iʿtibār al-maʾālāt (consequence analysis), and al-ḥukm (ruling). Four constructed scenarios drawn from documented industry practice illustrate the procedure across digital-banking, fintech and data-brokerage settings. Findings — Data and trained models can be characterised as māl mutaqawwam (lawfully appropriable property), but only after the object of the ruling is disaggregated into three tiers: the personal datum, which remains attached to a non-pecuniary right and is licenced rather than alienated; the compiled dataset, which becomes appropriable through documented effort and exclusive control (ikhtiṣāṣ); and the trained model, which is best read as a ḥaqq mālī (pecuniary right). Permissibility is conditional on three calibrated requirements: genuine riḍā (consent), the reduction of gharar (uncertainty) assessed across object, contract, and outcome, and the exclusion of prohibited ends governed by a probability-based standard under sadd al-dharāʾiʿ (blocking the means). Where these hold, a ruling of ibāḥah muqayyadah (conditional permissibility) follows the ruling. Originality/Value — The study moves fiqh al-muʿāmalāt al-raqamiyyah (the jurisprudence of digital transactions) from broad ethical exhortation towards a reproducible juristic procedure. Its specific contributions are a layered takyīf (characterisation) of digital assets, a three-layer calibration of gharar, a constructive-knowledge standard for downstream liability under sadd al-dharāʾiʿ, and a fuḍūlī (unauthorised-agent) reading that grounds human oversight of adaptive algorithms in the law of agency rather than in generic appeals to accountability. Research Limitations/Implications — As a conceptual study, the framework requires empirical testing in institutional settings, particularly in Islamic digital banking. Subsequent work should examine its operation for data fiduciaries and the governance of artificial intelligence. Practical Implications — The framework offers Sharīʿah boards, regulators, and developers an auditable basis for designing consent architectures, modelling disclosure regimes, and establishing recourse mechanisms within Islamic financial institutions. Social Implications — By anchoring data practices in justice (ʿadl), trust (amānah), and dignity protection, the framework supports ethical accountability in the digital economy.