Predictive Analytics and Recommendation Systems in Banking

The banking industry has undergone a paradigm shift with the rise of artificial intelligence (AI) predictive analytics and recommendation systems. Artificial intelligence, machine learning, and natural language processing are revolutionizing financial services — they allow for real-time fraud detection, tailored financial product recommendations, and consumer segmentation. Banking Sector: The Impact of Machine Learning Algorithms on Customer Engagement, Risk Management and Decision Making. The study aligns with a hybrid recommendation model and a supervised fraud detection system to optimize banking operations. Customer segmentation using clustering-based methods increases personalization, and predictive analytics have shown improved fraud detection accuracy at 92% using random forest classifiers. These developments enhance customer experience, mitigate financial risks, and increase profitability.

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Publication Details

Published
2025-06-05
DOI
https://doi.org/10.1109/iccsp64183.2025.11089448
Primary Topic
Big Data and Business Intelligence
Type
article
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article

Predictive Analytics and Recommendation Systems in Banking

G. L. Saini, A. G. Abishek, M. N. Jagadish, Kalaiselvan Sivalingam Ambigapathi
Big Data and Business Intelligence
article

Predictive Analytics and Recommendation Systems in Banking

G. L. Saini, A. G. Abishek, M. N. Jagadish, Kalaiselvan Sivalingam Ambigapathi
article en

Abstract

The banking industry has undergone a paradigm shift with the rise of artificial intelligence (AI) predictive analytics and recommendation systems. Artificial intelligence, machine learning, and natural language processing are revolutionizing financial services — they allow for real-time fraud detection, tailored financial product recommendations, and consumer segmentation. Banking Sector: The Impact of Machine Learning Algorithms on Customer Engagement, Risk Management and Decision Making. The study aligns with a hybrid recommendation model and a supervised fraud detection system to optimize banking operations. Customer segmentation using clustering-based methods increases personalization, and predictive analytics have shown improved fraud detection accuracy at 92% using random forest classifiers. These developments enhance customer experience, mitigate financial risks, and increase profitability.

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