Exploratory Data Analysis for Banking and Finance: Unveiling Insights and Patterns

TL;DR

This paper demonstrates how Exploratory Data Analysis techniques can uncover valuable insights and patterns in banking and finance data, aiding decision-making and customer retention strategies.

Contribution

It provides a comprehensive step-by-step EDA approach applied to banking data, highlighting insights into customer behavior and churn factors.

Findings

Identification of transaction and usage patterns

Correlation between demographics and behavior

Factors influencing customer churn

Abstract

This paper explores the application of Exploratory Data Analytics (EDA) in the banking and finance domain, focusing on credit card usage and customer churning. It presents a step-by-step analysis using EDA techniques such as descriptive statistics, data visualization, and correlation analysis. The study examines transaction patterns, credit limits, and usage across merchant categories, providing insights into consumer behavior. It also considers demographic factors like age, gender, and income on usage patterns. Additionally, the report addresses customer churning, analyzing churn rates and factors such as demographics, transaction history, and satisfaction levels. These insights help banking professionals make data-driven decisions, improve marketing strategies, and enhance customer retention, ultimately contributing to profitability.