Neuro-symbolic artificial intelligence (NSAI) is a novel paradigm that combines the pattern recognition capabilities of neural networks with the logical reasoning of symbolic systems [11, 13]. This study explores the application of NSAI for detecting fraudulent activities on the Ethereum blockchain, employing an existing implementation known as Logic Tensor Networks (LTNs) [12, 13]. The task is formulated as a binary classification aimed at distinguishing between legitimate and fraudulent transactions. Ethereum is a decentralized, open-source blockchain, peer-to-peer platform based on the Kademlia protocol and powered by the Ethereum Virtual Machine (EVM), which enables the creation and execution of smart contracts and decentralized applications (dApps). Smart contracts are self-executing programs on the blockchain that automatically verify and enforce the terms of an agreement in a trustless manner. Once deployed, they become immutable and publicly auditable on the blockchain. The decentralized and pseudonymous nature of Ethereum presents significant challenges for effective fraud detection. In this study, we propose and evaluate a voting-based ensemble NSAI framework to classify fraudulent transactions using a publicly available Ethereum fraud detection dataset [14]. Experimental results demonstrate that the proposed ensemble architecture outperforms approximately 80% of the individual models. Moreover, the proposed voting approach offers natural compatibility within the peer-to-peer based Ethereum architecture.