Abstract:
Abstract—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 naïve 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 Naïve 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.
Keywords—Hidden Markov Model; cardholder transaction
patterns; merchant categories; predictive algorithms
