Despite the rapid growth of AI-enabled recommendations, encouraging borrowers to adopt recommendations in AI-enabled loan recommendation service (AI-LRS) remains a challenge. Based on the elaboration likelihood model and trust transfer theory, this study develops a research model to investigate the factors and internal mechanisms that influence borrowers’ intention to adopt recommended loan scheme. A scenario-based survey was conducted to gather 496 valid samples, and structural equation modeling was used to test the model. The results show that both central cues (loan scheme quality, reputation of lending institution, and structural assurance) and peripheral cues (social influence and trust propensity) positively impact borrowers’ trust in AI-LRS platform. Additionally, loan scheme quality, reputation of lending institution, and trust propensity are positively associated with borrowers’ trust in loan scheme. Moreover, trust in AI-LRS platform positively influences trust in loan scheme, and both types of trust significantly increase adoption intention. Notably, repayment pressure negatively moderates the relationship between trust in loan scheme and adoption intention, while it positively moderates the relationship between trust in AI-LRS platform and adoption intention.