Dynamic Behavior-Based Churn Forecasts in the Insurance Sector  

作　　者：Nagaraju Jajam Nagendra Panini Challa 

机构地区：[1]School of Computer Science and Engineering(SCOPE),VIT-AP University,Amaravati,522237,India

出　　处：《Computers, Materials & Continua》2023年第4期977-997,共21页计算机、材料和连续体（英文）

摘　　要：In the insurance sector, a massive volume of data is being generatedon a daily basis due to a vast client base. Decision makers and businessanalysts emphasized that attaining new customers is costlier than retainingexisting ones. The success of retention initiatives is determined not only bythe accuracy of forecasting churners but also by the timing of the forecast.Previous works on churn forecast presented models for anticipating churnquarterly or monthly with an emphasis on customers’ static behavior. Thispaper’s objective is to calculate daily churn based on dynamic variations inclient behavior. Training excellent models to further identify potential churningcustomers helps insurance companies make decisions to retain customerswhile also identifying areas for improvement. Thus, it is possible to identifyand analyse clients who are likely to churn, allowing for a reduction in thecost of support and maintenance. Binary Golden Eagle Optimizer (BGEO)is used to select optimal features from the datasets in a preprocessing step.As a result, this research characterized the customer’s daily behavior usingvarious models such as RFM (Recency, Frequency, Monetary), MultivariateTime Series (MTS), Statistics-based Model (SM), Survival analysis (SA),Deep learning (DL) based methodologies such as Recurrent Neural Network(RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU),and Customized Extreme Learning Machine (CELM) are framed the problemof daily forecasting using this description. It can be concluded that all modelsproduced better overall outcomes with only slight variations in performancemeasures. The proposed CELM outperforms all other models in terms ofaccuracy (96.4).

关 键 词：Customer churn customized extreme learning machine deep learning survival analysis RFM MTS SM BGEO 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]