Machine Learning-Driven Behavioural Insights into Customer Expectations for Personalized Banking Services

Abstract

Introduction: This study aims to explore the domain of behavioural finance using machine learning algorithms to derive conclusions about the customers in order to improve as well as personalize the services rendered. By employing machine learning and statistical analysis on banking data, the study provides actionable insights into how financial institutions can tailor their services to match distinct customer segments

Objectives: To separate the population data into 3 different segments and to test Hypothesis to ascertain the influence and expectations of personalization on customers, testing it based on the frequency of transactions, their age groups, and the average balance amount.

Methods: The study is carried out by utilizing a secondary data. An exploratory data analysis (EDA) is done to understand the demographic and transactional patterns found among the customers. K-means clustering algorithm is deployed to segment customers into distinctive behavioural profiles based on age, credit usage, frequency of transaction and account tenure. Random forest algorithm was used to predict customer preferences with regard to banking services.

Results: The findings throw light on the significance of demographic and transactional profiling in enabling precise personalization of banking experiences.

Conclusions: The study has outlined the distinguishable behavioural patterns that highlights expectations of personalized services. It is observed that transaction behaviour, credit usage and account longevity indicate to digital preferences. The segmentation suggests that personalization should differ from customer to customer based on their banking activities and the age group they belong to. These findings will be of use in behavioural finance literature as it is contextualizing the customer behaviour in a digital banking environment.