Are They Likely to Complain on Phish or Spam? A Prediction Model

Customers are the core of businesses. Specifically, telecommunication companies, customer satisfaction is considered to be a commercial necessity and therefore a priority. High rates of customer satisfaction increase both retention and attraction rates. As a result, telecommunication companies are always seeking new means to achieve these objectives. A large volume of calls is received in a typical call center from customers complaining about phishing or spam attacks daily. It is difficult to identify the purpose of the call manually. In this work, we expand on previous efforts to focus more on impacted phone spam or phish consumers. The study focuses on both mediums of communication, phone calls and messages. A historical sample of customers' complaints dataset was used, and several machine learning classification algorithms were applied to analyze the calls. These are Logistic Regression, XGBoost, Gradient Boosting, Random Forest, CatBoost, KNN, and SVM. The predictive model can identify whether an individual is likely to complain about a spam or phish attack. The performance of the baseline classifier achieves an accuracy of 63.4 % that is based on CatBoost. Moreover, the model identifies consumers' demographics. The findings show that people of age 45 are more likely to complain and that males are less likely to complain.