Distributed model for customer churn prediction using convolutional neural network

Purpose>The purpose of the proposed model is to assist the e-business to predict the churned users using machine learning. This paper aims to monitor the customer behavior and to perform decision-making accordingly.Design/methodology/approach>The proposed model uses the 2-D convolutional neural network (CNN;a technique of deep learning). The proposed model is a layered architecture that comprises two different phases that are data load and preprocessing layer and 2-D CNN layer. In addition, the Apache Spark parallel and distributed framework is used to process the data in a parallel environment. Training data is captured from Kaggle by using Telco Customer Churn.Findings>The proposed model is accurate and has an accuracy score of 0.963 out of 1. In addition, the training and validation loss is extremely less, which is 0.004. The confusion matric results show the true-positive values are 95% and the true-negative values are 94%. However, the false-negative is only 5% and the false-positive is only 6%, which is effective.Originality/value>This paper highlights an inclusive description of preprocessing required for the CNN model. The data set is addressed more carefully for the successful customer churn prediction.

Capital regulation, competition and risk-taking: Policy implications for banking sector stability in the MENA region

This paper investigates how capital requirements and bank competition affect banks' financial soundness in the Middle East and North Africa (MENA) region. We test the hypothesis that regulatory capital positively impacts the risk-taking behavior of Islamic and conventional banks in the MENA region. The analysis indicates that the capital adequacy ratio has no significant influence on the credit risk of Islamic banks;however, market competition does play a significant role in shaping the risk behavior of these institutions. We report the opposite result for conventional banks – an increase in the minimum capital requirements is associated with an increase in their risk level. We also find that Islamic and conventional banks experience a non-linear relationship between market concentration and bank risk. Our findings inform regulatory authorities concerned with improving banking sector’s stability in the MENA region to strengthen their policies to force banks to better align with capital requirements which is highly important during the COVID-19 pandemic.

Competition, capital growth and risk-taking in emerging markets: Policy implications for banking sector stability during COVID-19 pandemic.

This paper investigates how banking competition and capital level impact on the risk-taking behavior of banking institutions in the Middle East and North Africa (MENA) region. The topic is perceived to be of significant importance during the COVID-19 pandemic. We use data for more than 225 banks in 18 countries in the MENA region to test whether increased competition causes banks to hold higher capital ratios. Employing panel data techniques, and distinguishing between Islamic and conventional banks, we show that banks tend to hold higher capital ratios when operating in a more competitive environment. We also provide evidence that banks in the MENA region increase their capitalization levels in response to a higher risk and vice versa. Further, banking concentration (measured by the HH-index) and credit risk have a significant and positive impact on capital ratios of IBs, whereas competition does play a restrictive role in determining the level of their capital. The results hold when controlling for ownership structure, regulatory and institutional environment, bank-specific and macroeconomic characteristics. Our findings inform regulatory authorities concerned with improving the financial stability of banking sector in the MENA region to strengthen their policies in order to force banks to better align with capital requirements and risk during the COVID-19 pandemic.