Combined rough set theory and flow network graph to predict customer churn in credit card accounts

[[abstract]]Customer churn has become a critical issue, especially in the competitive and mature credit card industry. From an economic and risk management perspective, it is important to understand customer characteristics in order to retain customers and differentiate high-quality credit customers from bad ones. However, studies have not yet adequately introduced rules based on customer characteristics and churn forms of original data. This study uses rough set theory, a rule-based decision-making technique, to extract rules related to customer churn; then uses a flow network graph, a path-dependent approach, to infer decision rules and variables; and finally presents the relationships between rules and different kinds of churn. An empirical case of credit card customer churn is also illustrated. In this study, we collect 21,000 customer samples, equally divided into three classes: survival, voluntary churn and involuntary churn. The data from these samples includes demographic, psychographic and transactional variables for analyzing and segmenting customer characteristics. The results show that this combined model can fully predict customer churn and provide useful information for decision-makers in devising marketing strategy

Churn classification model for local telecommunication company based on rough set theory

Customer care plays an important role in a company especially in managing churn for Telecommunication Company. Churn is perceived as the behaviour of a customer to leave or to terminate a service. This behaviour causes the loss of profit to companies because acquiring new customer requires higher investment compared to retaining existing ones. Thus, it is necessary to consider an efficient classification model to reduce the rate of churn. Hence, the purpose of this paper is to propose a new classification model based on the Rough Set Theory to classify customer churn. The results of the study show that the proposed Rough Set classification model outperforms the existing models and contributes to significant accuracy improvement.Keywords: customer churn; classification model; telecommunication industry; data mining;rough set

Customer Churn Prediction in Telecom Sector: A Survey and way a head

© 2021 International Journal of Scientific & Technology Research. This work is licensed under a Creative Commons Attribution 4.0 International License.The telecommunication (telecom)industry is a highly technological domain has rapidly developed over the previous decades as a result of the commercial success in mobile communication and the internet. Due to the strong competition in the telecom industry market, companies use a business strategy to better understand their customers’ needs and measure their satisfaction. This helps telecom companies to improve their retention power and reduces the probability to churn. Knowing the reasons behind customer churn and the use of Machine Learning (ML) approaches for analyzing customers' information can be of great value for churn management. This paper aims to study the importance of Customer Churn Prediction (CCP) and recent research in the field of CCP. Challenges and open issues that need further research and development to CCP in the telecom sector are exploredPeer reviewe

Customer retention

A research report submitted to the Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg, in partial fulfillment of the requirements for the degree of Master of Science in Engineering. Johannesburg, May 2018The aim of this study is to model the probability of a customer to attrite/defect from a bank where, for example, the bank is not their preferred/primary bank for salary deposits. The termination of deposit inflow serves as the outcome parameter and the random forest modelling technique was used to predict the outcome, in which new data sources (transactional data) were explored to add predictive power. The conventional logistic regression modelling technique was used to benchmark the random forest’s results. It was found that the random forest model slightly overfit during the training process and loses predictive power during validation and out of training period data. The random forest model, however, remains predictive and performs better than logistic regression at a cut-off probability of 20%.MT 201

Sistema inteligente basado en redes neuronales, máquina de soporte vectorial y random forest para la predicción de deserción de clientes en microcréditos de bancos

La deserción de clientes bancarios es un problema que afecta actualmente a las empresas de todos los sectores y en todos los países. Por su parte, el sector financiero es uno de los más importantes debido a la gran cantidad de clientes y dinero que estos aportan. Las empresas invierten dinero para realizar un seguimiento a los clientes y poder identificar patrones que puedan evidenciar si un cliente va a dejar de hacer negocios con la empresa, pero muchas veces las maneras manuales de realizarlas presentan deficiencias de tiempo y de pérdida de dinero. En la literatura es común ver modelos de predicción de deserción de clientes bancarios microcréditos, el punto débil de estos es que solo aplican una técnica para realizar propiamente la predicción. En virtud de esto, se propone un sistema inteligente basado en un modelo híbrido que combina tres técnicas para proporcionar mejor precisión que la observada en la literatura; estas son Máquinas de Soporte Vectorial, Redes Neuronales y Random Forest. Los resultados numéricos obtenidos del experimento realizado a un banco peruano con un conjunto de datos de 24 420 clientes presentan una precisión de 97.38%, el cual mejora los resultados de la literatura

Can bank interaction during rating measurement of micro and very small enterprises ipso facto Determine the collapse of PD status?

This paper begins with an analysis of trends - over the period 2012-2018 - for total bank loans, non-performing loans, and the number of active, working enterprises. A review survey was done on national data from Italy with a comparison developed on a local subset from the Sardinia Region. Empirical evidence appears to support the hypothesis of the paper: can the rating class assigned by banks - using current IRB and A-IRB systems - to micro and very small enterprises, whose ability to replace financial resources using endogenous means is structurally impaired, ipso facto orient the results of performance in the same terms of PD assigned by the algorithm, thereby upending the principle of cause and effect? The thesis is developed through mathematical modeling that demonstrates the interaction of the measurement tool (the rating algorithm applied by banks) on the collapse of the loan status (default, performing, or some intermediate point) of the assessed micro-entity. Emphasis is given, in conclusion, to the phenomenon using evidence of the intrinsically mutualistic link of the two populations of banks and (micro) enterprises provided by a system of differential equation

Churn prediction models tested and evaluated in the Dutch indemnity industry

Due to global developments customer churn is getting a growing concern to the insurance industry. Technological improvements like the internet makes it much easier for customer to compare their policies, obtain new offers or even churn from one provider to another. The insurance industry therefore has become a heavily competitive market in which insurance companies have to compete to protect and expand their customer base in order to maintain or expand their market position. Thus, retaining customers is becoming more and more important and therefore finding customers who are most likely to leave is a central aspect. Many different techniques are available to identify customers who are most likely to leave, however which technique can be used best is often not clear. Research clarifies that the characteristics of the industry and/or dataset which is used are mostly assessing related to performance. In advance it is impossible to determine the best suited technique to use if previous research in which performance was tested has not been published. This study presents a data mining methodology in which the four most used prediction techniques in literature are tested and evaluated using a real life voluminous insurance company dataset to determine which technique performs best. Using the same dataset makes results comparable and clears out which technique performs best based on the insurance data domain characteristics

Customer churn prediction in telecommunication industry using data certainty

Customer Churn Prediction (CCP) is a challenging activity for decision makers and machine learning community because most of the time, churn and non-churn customers have resembling features. From different experiments on customer churn and related data, it can be seen that a classifier shows different accuracy levels for different zones of a dataset. In such situations, a correlation can easily be observed in the level of classifier's accuracy and certainty of its prediction. If a mechanism can be defined to estimate the classifier's certainty for different zones within the data, then the expected classifier's accuracy can be estimated even before the classification. In this paper, a novel CCP approach is presented based on the above concept of classifier's certainty estimation using distance factor. The dataset is grouped into different zones based on the distance factor which are then divided into two categories as; (i) data with high certainty, and (ii) data with low certainty, for predicting customers exhibiting Churn and Non-churn behavior. Using different state-of-the-art evaluation measures (e.g., accuracy, f-measure, precision and recall) on different publicly available the Telecommunication Industry (TCI) datasets show that (i) the distance factor is strongly co-related with the certainty of the classifier, and (ii) the classifier obtained high accuracy in the zone with greater distance factor's value (i.e., customer churn and non-churn with high certainty) than those placed in the zone with smaller distance factor's value (i.e., customer churn and non-churn with low certainty)

Forecasting modeling and analytics of economic processes

The book will be useful for economists, finance and valuation professionals, market researchers, public policy analysts, data analysts, teachers or students in graduate-level classes. The book is aimed at students and beginners who are interested in forecasting modeling and analytics of economic processes and want to get an idea of its implementation