Using Search Trends to Optimize Financial Product Recommendations: An AI Approach

Arun Kumar Manimaran

doi:10.36948/ijfmr.2023.v05i01.45570

Using Search Trends to Optimize Financial Product Recommendations: An AI Approach

Author(s)	Arun Kumar Manimaran
Country	United States
Abstract	Being able to provide product suggestions that suit each consumer is very important in the rapidly changing world of finance. The study looks into how AI and search trend analysis work together to help personalize financial product recommendations. Data from search and social media is used to spot trends in people’s behavior, helping financial firms create offerings that are in line with consumers’ needs. According to the study, acting on real-time consumer insights helps increase customer satisfaction and strength of loyalty. It involves using machine learning together with NLP techniques to analyze and understand the text. We go through many different search queries about financial products, finding out what consumers are most interested in. By grouping search trends using clustering, we help financial institutions target specific consumers and their desired financial products. Thanks to this method, businesses can advise customers on a personal level and also adjust to upcoming market trends to maintain an edge over others. Moreover, the results of our research can help more than just the people we directly treat. They give banks and other financial institutions the means to track trends in the market and how people feel about them. Using AI, firms in the financial industry can offer more effective and adapted services to meet the preferences of their customers. Using these two forces together, through AI combined with search data, can significantly improve the way fintech companies design financial products and reach their customers. Proper use of these insights allows financial institutions to become more sustainable and continue to grow successfully in a tough market.
Keywords	Search Trends; Financial Product Recommendations; Artificial Intelligence; Machine Learning; Predictive Analytics; Consumer Behavior; Search Engine Optimization; Natural Language Processing; User Intent; Data Mining; Personalization; Recommender Systems; Fintech; Digital Marketing; Keyword Analysis; User Experience; Behavioral Analytics; Search Query Analysis; Financial Services; AI Algorithms; Customer Segmentation; Big Data; Semantic Search; Investment Platforms; Search Behavior; AI-Driven Insights; Financial Technology; Trend Analysis; Personalized Marketing; Financial Decision-Making
Field	Engineering
Published In	Volume 5, Issue 1, January-February 2023
Published On	2023-01-05
DOI	https://doi.org/10.36948/ijfmr.2023.v05i01.45570
Short DOI	https://doi.org/g9kt5r

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Using Search Trends to Optimize Financial Product Recommendations: An AI Approach

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