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.
Authors
- G. L. Saini (ORCID: https://orcid.org/0000-0003-4008-3309)
- A. G. Abishek
- M. N. Jagadish
- Kalaiselvan Sivalingam Ambigapathi
Institutions
- Artificial Intelligence in Medicine (Canada) (CA)
Publication Details
- Published
- 2025-06-05
- DOI
- https://doi.org/10.1109/iccsp64183.2025.11089448
- Primary Topic
- Big Data and Business Intelligence
- Type
- article
- Field-Weighted Citation Impact
- 0.00