Customer Retention in Banking: Utilizing AI and Machine Learning for Predictive Churn Analysis

Customer retention in the banking sector is a practice by which these organizations have adopted measures in order to ensure the existing clients to continue to maintain accounts with agencies. This is important for banks in particular since customer retention is normally considerably less expensive than customer acquisition. Prior studies have not been very successful in providing satisfactory explanations and means of predicting customer churn Specifically, most analyses, including traditional ML approaches, are inadequate at handling transactional data with rich behavioral patterns and temporal dependencies. This study, however, fills these gaps by adopting a hybrid model, autoencoder model and Long Short-Term Memory (LSTM) model integrated approach. The method uses the autoencoder’s ability of transforming high-dimensional input data such as transaction data to a lower-dimensional form, thereby removing noise in the process and preserving useful characteristics. The next LSTM model takes into account chronological patterns examined in customers’ behavior, thus making churn predictions even more precise. Worked in python, this model was trained and tested on the data-set containing numerous attributes of the transaction type effective with 99.4% accuracy. The outcomes showed better performances in comparison with conventional approaches, suggesting that DL algorithms could be very beneficial in the frameworks of the predictive analytics for customer retention in the banking industry.