Recommender System for Banking Industry with Collaborative Filtering and XGBoost Classifier

As netizens, we interact with recommendation systems daily - while using movie or music streaming services, dating apps, shopping online, browsing social media, or while usual Google searches. Recommendation systems are one of the most popular and heavily used AI applications. Amazon and Netflix use machine learning and AI to power up their recommendation systems. These systems can effectively increase sales, revenue, click-through rates, and customer conversions. Customizing product or content recommendations based on the preferences of a particular user creates a positive effect on the user experience. This FinTech system can be a revolution in the banking industry by assisting banks in growing revenue, containing costs, providing customer satisfaction, and bringing brand loyalty. This study aims to improve customer engagement in the banking sector by providing suitable product recommendations to the right customer using a combination of collaborative filtering and classification approaches. In this work, a hybrid recommendation model is proposed to provide recommendations to banks' banking clients. The model is built on a Python environment to address various challenges related to recommendation systems in the banking industry.