Artificial Intelligence-Powered Personal Finance Management with Short Message Service Bill Detection: A Comprehensive Review

Abstract

The rising trend in the application of digital banking, online payments, and billing, which is done using the short message service, has complicated the personal financial management of individuals. The excessive number of unstructured financial messages the users receive is a major contributor to their inability to identify their expenditures, manage their bills, and practice financial discipline. The given paper provides the detailed review of Artificial Intelligence-based personal finance management system with the specific emphasis on bill detection via short message service and transaction analysis. This review aims at discussing the current research methods, defining the most important techniques and outlining the limitations in the field. The relevant literature was identified in the recent journals and conference papers that focus on Natural Language Processing based message parsing, machine learning-based expense classification, financial analytics, and intelligent advising systems. The key trends mentioned in the review include the application of Natural Language Processing to extract financial data in messages and machine learning models that can be used to categorize expenses and identify anomalies. Nevertheless, research gaps, such as a lack of integration between short message service data and bank and card transactions, lack of predictive financial information, and a lack of personalization in advisory systems are also identified in the analysis. The review adds value to this Information Systems field by offering a conceptualized finding of the existing work as well as offering future research perspectives on which more complex and smart solutions can be developed in the personal finance management.