Trust and Perceived Risk in Artificial Intelligence Use for Forex and Cryptocurrency Trading

Erwin Halim, C Van, Shara Toibayeva, Jefferson Cristoval, Laura Dorothy Thomason, Ai Ping Teoh

2026

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DOI 10.1109/isiber68248.2026.11469785
Data confidence 70 medium
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Abstract

This study investigates the determinants of the utilization of Artificial Intelligence (AI) as a trading tool in forex and cryptocurrency trading by integrating psychological and technological variables. The user of the AI tool did not understand the AI method used in the tool. As forex or cryptocurrency traders, their concern is how to trade and profit. Using purposive sampling, data were collected from 486 respondents residing in the Greater Jakarta area (Jabodetabek), Indonesia, between May and June 2025. Respondents consisted traders with varying levels of experience. The data were analyzed using Structural Equation Modeling (SEM) with the Partial Least Squares (PLS) approach through SmartPLS 4.0 to evaluate causal relationships based on Technology Acceptance Model (TAM) among seven constructs: Perceived Risk, Data Security, Trust, Perceived Ease of Use, Perceived Usefulness, Attitude, and AI Use. Results indicate that all hypothesized relationships are statistically significant. Attitude and Trust strongly influence AI Use, while Perceived Risk also affects Attitude and Data Security. Moreover, Perceived Ease of Use enhances both Perceived Usefulness and Trust, highlighting the importance of user experience and system design. The model explains 72.4% of the variance in AI Use, demonstrating strong predictive capability. These findings contribute to digital finance theory by integrating Trust, perceived risk, and technological readiness as key enablers of AI adoption in speculative markets. Practically, the results emphasize the importance of transparent, secure, and userfriendly AI systems to strengthen user confidence.