Decision support systems that evaluate user decisions have the potential to improve financial decision-making by alerting users to potentially disadvantageous choices. However, the feasibility of such systems, especially in complex decision-making scenarios, remains underexplored. This work in progress aims to investigate to what extend EEG-based decision support systems can be implemented using current technology. In a pilot study, we adapted the Iowa Gambling Task, a well-established decision-making paradigm, and collected 33-channel EEG data from three participants. As a proof of concept, we used a convolutional neural network (EEGNet) to classify positive and negative feedback, achieving subject-dependent binary classification accuracies ranging from 67 to 75%. These findings demonstrate the potential for developing and evaluating decision support systems that detect suboptimal decisions in real-world financial applications.