Bank Customer Churn Prediction Model

EasyChair Preprint 12533

Abstract

In the dynamic realm of banking, accurately anticipating and managing customer churn stands as a critical imperative for sustaining business viability and fostering growth. This abstract outlines a comprehensive approach to building and deploying predictive models tailored to identify and mitigate bank customer churn.

Our model integrates a diverse range of data streams, encompassing demographic profiles, transaction histories, customer interactions, and external economic factors. Employing sophisticated machine learning algorithms—such as logistic regression, decision trees, random forests, and gradient boosting machines—we aim to unveil nuanced patterns and correlations inherent in the data.

Feature engineering assumes a pivotal role in augmenting the model's predictive prowess. Techniques such as principal component analysis (PCA), feature selection algorithms, and time-series analysis are harnessed to extract actionable insights. Furthermore, the model incorporates methodologies to address imbalanced datasets and mitigate the influence of rare events, ensuring resilience in real-world scenarios.

Keyphrases: Adaptability, Bank customer churn, Customer Behaviors, Customer relationship management (CRM) systems, Financial Services Industry, Imbalanced datasets, Machine Learning Algorithms, Retention Strategies, Validation and evaluation, continuous monitoring, feature engineering, market dynamics, performance metrics, predictive modeling, resource allocation

@booklet{EasyChair:12533,
  author    = {Vedant Sharma},
  title     = {Bank Customer Churn Prediction Model},
  howpublished = {EasyChair Preprint 12533},
  year      = {EasyChair, 2024}}