Citation :

Arpitha C N, Sunitha M R, "Optimized Convolutional Neural Network for Accurate Personality Trait Prediction from Spending Data," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 1, pp. 117-127, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I1P109

Abstract

Understanding the link between a person's spending data and their personality traits can significantly improve applications in psychology, financial advising, and targeted marketing. This study presents an optimized Convolutional Neural Network (CNN) model to predict big five personality traits: Openness, Conscientiousness, Extraversion, Agreeableness, Neuroticism (OCEAN), and also materialism and self-control based on debit transaction data. Three main aspects of spending behavior are analyzed: overall spending behavior, category-related spending behavior, and customer category profiles. The study addresses two main research questions: (1) Can spending patterns from transaction data reliably indicate a person's psychological traits? (2) How does an optimized CNN model compare to traditional machine learning models in predicting personality? The proposed model uses Pearson correlation for feature selection and extraction, then applies deep learning for behavioral inference. Experimental results show that the CNN model achieves a classification accuracy of 99.72%, surpassing conventional methods. These findings confirm that spending data can effectively represent personality profiling, providing data driven insights into consumer psychology.

Keywords

Convolutional Neural Network, Deep Learning, Personality Trait, Psychological Profiles, Spending Data.

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