In this research, the application of neuromarketing to consumer financial decision making with the use of data driven algorithm is explored. The study will integrate EEG signals, machine learning techniques and behavioral analytics to predict consumer choices and emotional response in the context of financial decisions. “Four classifier algorithms, utilizing Neural Networks (NN), Support Vector Machines (SVM), Decision Trees (DT), Random Forests (RF), were employed to analyse and classify the consumer data”. The neural network model had an accuracy of 92% for detecting emotional responses to financial decisions which was better than other algorithms. Decision trees and random forests gave us interpretable insights with 85% and 87% accuracies, and followed closely behind with 89% accuracy by support vector machines. These results show that despite possessing more predictive power, deep learning models cannot outperform simpler models such as decision trees for practical applications due to lack of interpretability. In addition, the ethical problem of the consumer’s privacy was broached and it is pointed out that the use of neuromarketing should be done in a responsible manner. The findings from this study show that using neuromarketing techniques in financial decision making are effective and give businesses a good deal of insight to improve customer engagement and optimize marketing.
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