Abstract
Recommender systems can be found nowadays in a variety of different domains and
industries, its popularity depends on the proven exponential enhancement of streaming
services and e-commerce businesses on recent times. However, most of the recommender
systems rely on the ratings given by users to the items. The major challenge for using
recommender systems in the banking industry is the absence of ratings. It is very unlikely that
a bank institution will ask customers to rate their products. To solve this problem, the
research implemented a rating algorithm that gives a weight depending on the number of
transactions and product usage from every customer of the dataset. Customer segmentation
was used for targeting the customers that were more prone to open and accept an investment
from the banking institution. PyTorch helped to develop a Neural Collaborative Filtering
model that was capable to predict the interaction between the customer and future banking
products. Finally, PySpark was used for the development of an ALS recommender system that
generated banking products recommendations to the customers. This research contributes
to future studies by experimenting with multiple recommender systems applied to the
banking industry that will increase the value of customers and support decision making on
implementing customer marketing campaigns.
