| dc.description.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. | en_US |
