Detecting Digital Investment Scams: The SDQC User-Level Screening Framework for Malaysian Gig Workers

Abstract

Digital investment scams targeting gig workers and freelancers in Malaysia have intensified with the expansion of cryptocurrency markets and AI-driven platforms, yet individuals in precarious employment lack access to structured tools for evaluating fraudulent opportunities. This study conceptualises digital scam vulnerability as a user-level decision support problem and develops the Scam Detection Quick-Check (SDQC), a six-dimensional screening framework derived from the synthesis of behavioural, regulatory, and informational scam risk indicators. The study employs a conceptual design supported by targeted literature synthesis and illustrative case analysis of three high-profile Malaysian investment scams: Lavida Coin, Harapan Coin, and JJPTR. The SDQC framework comprises six screening dimensions, namely Source Verification, Credibility Assessment, Offering Clarity, Disclosure and Regulation, External Validation, and Timing Awareness, each mapped to a distinct class of scam vulnerability identified in the literature. Case analysis shows that all three Malaysian cases failed across multiple SDQC dimensions, illustrating the framework’s diagnostic relevance in real-world scam contexts. The study makes three scholarly contributions: it reframes individual scam susceptibility as a structured screening problem; it develops the SDQC from literature synthesis and case-based reasoning; and it extends scam prevention research into the digital gig economy context in Malaysia. The SDQC offers a practical user-level screening tool applicable to financial literacy programmes, platform-integrated warning systems, and regulatory guidance in Malaysia and comparable digital labour markets.