Machine Learning Applications in Customer Segmentation and Profit Optimization for Digital Payment Vendors

P. Manjula Devi, S. Vinoth and Gopalakrishnan Chinnasamy ()
Additional contact information
P. Manjula Devi: CMS Business School, Jain Deemed-to-be University, Research Scholar, Faculty of Management
S. Vinoth: CMS Business School, Jain Deemed-to-be University, Professor, Faculty of Management
Gopalakrishnan Chinnasamy: CMS Business School, Jain Deemed-to-be University, Professor, Faculty of Management

A chapter in Proceedings of the 3rd International Conference on Artificial Intelligence in Economics, Finance and Management (ICAIEFM 2025), 2025, pp 213-229 from Springer

Abstract: Abstract The evolution of electronic payment systems, re-defining fraud detection, vendor profitability, and customer loyalty. This study examines how machine learning models improve profit maximization and customer segmentation in electronic payment systems. Based on a mixed-methods design, data were collected through structured surveys from 205 participants and statistical analysis and predictive modelling. Machine learning algorithms such as K-Means Clustering, Multiple Linear Regression, Random Forest, and Autoencoder-based Anomaly Detection were used to assess transaction patterns, retention patterns, and patterns of fraud risk. The findings suggest that payment frequency, platform diversification, and digital payment adoption are the most powerful drivers of vendors’ profitability. The study establishes that customer segmentation and targeted offers propel customer retention while deep learning algorithms significantly enhance the accuracy of fraud detection. Vendors, policymakers, and financial institutions have implementable facts from these studies to increase digital payment adoption, anti-fraud, and customer engagement. Future studies should be directed toward fraud detection models in real time as well as adaptive reinforcement learning to ensure maximum digital transaction security and efficiency.

Keywords: Digital Payments; Machine Learning; Customer Segmentation; Vendor Profitability; Fraud Detection (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-896-7_12

Ordering information: This item can be ordered from
http://www.springer.com/9789464638967

DOI: 10.2991/978-94-6463-896-7_12

Access Statistics for this chapter

More chapters in Advances in Economics, Business and Management Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().