Optimizing Financial Decision Support Systems with Machine LearningDriven Recommendations

Abstract

The research investigates the utility of cosine similarity as an innovative recommendation system designed to assist individuals in making financial choices tailored to their unique preferences and objectives. It embarks on an extensive analysis of diverse datasets encompassing a wide array of financial products, including investment portfolios, credit card offerings, insurance plans, personal loan options, and car loan packages. Each dataset undergoes meticulous feature extraction and preprocessing to optimize the accuracy of the cosine similarity model. The research then applies cosine similarity to calculate the similarity scores between individual financial products, thereby producing personalized recommendations. These recommendations are predicated on a comprehensive spectrum of input variables. The outcomes of these case studies demonstrate the potency of cosine similarity as a foundation for the development of tailored financial guidance systems. Such recommendations empower individuals to make informed decisions that are intrinsically aligned with their distinctive financial aspirations. Ridge and lasso regression algorithms are deployed to develop predictive models for assessing investment preferences and evaluating potential investment returns. The study highlights the necessity for financial institutions and advisory platforms to invest in data quality and algorithmic sophistication to enhance the efficacy and accuracy of these financial recommendations.