Publicly Disclosed Data-Driven Decisions in SPayLater Digital Financing: A Shariah Accountability Analysis
DOI:
https://doi.org/10.24256/jiis.v6i1.12438Keywords:
SPayLater, Shariah Accountability, digital financing, Automated Decision MakingAbstract
This study examines publicly disclosed information on data-driven and algorithmically assisted decision-making in SPayLater digital financing, viewed from the perspective of Shariah Accountability. The study employs a qualitative document analysis within a bounded case. Data were drawn from a purposively selected corpus of publicly available documents, comprising the Product and Service Information Summary (RIPLAY), the Terms and Conditions of Service, the Privacy Policy, official reports, and FAQ pages of PT Commerce Finance/SPayLater, together with relevant academic literature (see Table 1 for the full document corpus), accessed through [state document access period]. As an assessment of publicly disclosed information rather than an audit of PT Commerce Finance's internal system, the data were analyzed thematically using the principles of amanah, mas'uliyyah, 'adl, ihsan, and accountability to Allah SWT. The findings indicate that public disclosures show data-mediated assessment activities-including user selection, credit scoring, eligibility and risk assessment, risk grading, and the determination of financing facilities and limits-that are consistent with some characteristics of automated or algorithmically assisted decision-making, although the precise degree of automation cannot be established from public sources alone. The study did not find publicly available information regarding the algorithms, formulas, variable weights, or machine learning models used by the system; the technical mechanism of the system therefore cannot be assessed in detail. From the perspective of Shariah Accountability, the use of technology in decision-making does not eliminate human and institutional responsibility. Responsibility remains with those who have the authority to design, operate, and oversee the system. Accordingly, such data-driven processes should be accompanied by responsible data management, fair decision-making, meaningful benefit for users, and adequate human oversight and correction mechanisms.
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