UCLA

Customer churn in the banking industry occurs when clients terminate their relationship with the bank, leading to significant losses in revenue and reputation. In today's highly competitive market, retaining customers is crucial. A strong customer base not only sustains the bank's revenue but also attracts new clients through trust and referrals from satisfied customers. Therefore, identifying and preventing customer churn is a critical task for banks. Our research utilized various machine learning algorithms to predict which customers are likely to leave. By analyzing the models, we identified patterns that serve as early warning signs of churn. Based on these valuable insights, we provide banks with recommendations on effective strategies to retain their customers.