International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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FinNext: An Intelligent System for Real-Time Financial Product Recommendation Using Customer Transaction Patterns and Market Dynamics

Author(s) Anirudh Reddy Pathe
Country United States
Abstract This paper presents FinNext, an intelligent recommendation system for real-time financial product suggestions based on customer transaction patterns and market dynamics. Leveraging advanced machine learning techniques, FinNext aims to enhance customer satisfaction and financial service providers' offerings by providing tailored recommendations based on individual financial behaviors and current market conditions. By integrating both transaction data and external market data, the system adapts to market fluctuations, optimizing financial advice for users in a dynamic economic environment. Few researches, meanwhile, have computed the similarity between utilizing product and customer data. As a result, a confusion matrix is used in this study to produce affinity variables that mix product and consumer data. In order to improve forecasting effectiveness in massive analysis data, a variety of derived variables are also developed. A sliding-window technique is taken into consideration to build the recommendation model in this study, which applies a variety of data mining classifiers, including decision trees, neural networks, support vector machines, random forests, and rotation forests.
Keywords Product recommendation model, Financial Product Recommendation, Real-Time Systems, Customer Transaction Patterns, Market Dynamics, Machine Learning, Personalization, Financial Technology (FinTech), Recommendation System.
Field Engineering
Published In Volume 5, Issue 1, January-February 2023
Published On 2023-01-10
Cite This FinNext: An Intelligent System for Real-Time Financial Product Recommendation Using Customer Transaction Patterns and Market Dynamics - Anirudh Reddy Pathe - IJFMR Volume 5, Issue 1, January-February 2023. DOI 10.36948/ijfmr.2023.v05i01.35260
DOI https://doi.org/10.36948/ijfmr.2023.v05i01.35260
Short DOI https://doi.org/g82hrq
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