Predicting Toll-Free Queries of Bank Using Machine Learning Algorithms

Toll-free numbers are one of the primary channels of customer support in the banking industry. Managing the volume of toll-free queries efficiently is essential to ensure customer satisfaction. However, predicting the number of toll-free queries in a day can be challenging due to the unpredictable nature of customer behavior. In this paper, we propose a machine learning approach to predict the number of toll-free queries in a day for banks. We use historical data of toll-free queries and external factors like holidays and promotions to train and test our model. Our results show that our approach can accurately predict the number of toll-free queries in a day, thus helping banks manage their customer support resources more efficiently. Banks receive numerous toll-free queries from their customers every day, and predicting the volume of queries can help banks better allocate their resources and provide timely assistance to their customers. In this study, we investigated the effectiveness of machine learning algorithms in predicting the volume of toll-free queries received by a bank in a day. We collected a dataset of toll-free queries received by a large banking institution in the United States over a period of six months. We applied various machine learning algorithms, including Linear Regression, Decision Trees, Random Forests, Gradient Boosting, and Support Vector Machines, to the dataset and evaluated their performance using the Root Mean Squared Error (RMSE) metric. The results showed that Gradient Boosting had the highest accuracy in predicting the volume of toll-free queries, with an RMSE of 10.9. The findings of this study suggest that machine learning algorithms can provide valuable insights to banks for predicting the volume of toll-free queries and optimizing their resources.