A Dynamic Classification Approach to Churn Prediction in Banking Industry

Churn prediction is the process of using transaction data to identify customers who are likely to cease their relationship with a company. To date, most work in churn prediction focuses on sampling strategies and supervised modeling over a short period of time. Few have explored the area of mining customer behavior pattern in longitudinal data. This research developed a dynamic approach to optimizing model specifications by using time-series predictors, multiple time periods, and rare event detection to enable accurate churn prediction. The study used a unique three-year dataset consisting of 32,000 transaction records of a retail bank in Florida, USA. It uses trend modeling to capture the change of customer behavior over time. Results show that data from multiple time periods helped to improve model precision and recall. This dynamic churn prediction approach can be generalized to other fields for which mining long term customer data is necessary

Dropout Prediction: A Systematic Literature Review

Dropout predicting is challenging analysis process which requires appropriate approaches to address the dropout. Existing approaches are applied in different areas such as education, telecommunications, retail, social networks, and banking services. The goal is to identify customers in the risk of dropout to support retention strategies. This research developed a systematic literature review to evaluate the development of existing studies to predict dropout using machine learning, following the guidelines recommended by Kitchenham and Peterson. The systematic review followed three phases planning, conducting, and reporting. The selection of the most relevant articles was based on the use of Active Systematic Review tool using artificial intelligence algorithms. The criteria identified 28 articles and several research lines where identified. Dropout is a transversal problem for several sectors of economic activity, where it can be taken countermeasures before it happens if detected early