Hidden Markov Model for Cardholder Purchasing Pattern Prediction

This study utilizes the Hidden Markov Model to predict cardholder purchasing patterns by monitoring card transaction trends and profiling cardholders based on dominant transactional motivations across four merchant sectors, i.e., service centers, social joints, restaurants, and health facilities. The research addresses shortfalls with existing studies which often disregard credit, prepaid, and debit card transactions outside online transaction channels, primarily focusing only on credit card fraud detection. This research also addresses the challenges of existing prediction algorithms such as support vector machine, decision tree, and na & iuml;ve Bayes classifiers. The research presents a three-phased Hidden Markov Model implementation starting with initialization, de-coding, and evaluation all executed through a Python script and further validated through a 2-fold cross- validation technique. The study uses an experimental design to systematically investigate cardholder transactional patterns, exposing training and validation data to varied initial and transition state probabilities to optimize prediction outcomes. The results are evaluated through three key metrics, i.e., accuracy, precision, and recall measures, achieving optimal performance of 100% for both accuracy and precision rates, with a 99% on recall rate, thereby outperforming existing predictive algorithms like support vector machine, decision tree, and Na & iuml;ve Bayes classifiers. This study proves the Hidden Markov Model's effectiveness in dynamically modeling cardholder behaviors within merchant categories, offering a full understanding of the real motivations behind card transactions. The implication of this research encompasses enhancing merchant growth strategies by empowering card acquirers and issuers with a better approach to optimize their operations and marketing synergies based on a clear understanding of cardholder transactional patterns. Further, the research significantly contributes to consumer behavior analysis and predictive modeling within the card payments ecosystem.