  The advent of Blockchain technology (BT) revolutionised the way remittance
transactions are recorded. Banks and remittance organisations have shown a
growing interest in exploring blockchain's potential advantages over
traditional practices. This paper presents a data-driven predictive decision
support approach as an innovative artefact designed for the blockchain-oriented
remittance industry. Employing a theory-generating Design Science Research
(DSR) approach, we have uncovered the emergence of predictive capabilities
driven by transactional big data. The artefact integrates predictive analytics
and Machine Learning (ML) to enable real-time remittance monitoring, empowering
management decision-makers to address challenges in the uncertain digitised
landscape of blockchain-oriented remittance companies. Bridging the gap between
theory and practice, this research not only enhances the security of the
remittance ecosystem but also lays the foundation for future predictive
decision support solutions, extending the potential of predictive analytics to
other domains. Additionally, the generated theory from the artifact's
implementation enriches the DSR approach and fosters grounded and stakeholder
theory development in the information systems domain.
