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Enhanced Deep Feed Forward Neural Network Model for the Customer Attrition Analysis in Banking Sector
9 references·1 citing·51 second hop
Enhanced Deep Feed Forward Neural Network Model for the Customer Attrition Analysis in Banking Sector (2019)Enhanced Deep Feed Forward Neural Network…Predicting Customer Churn: Extreme Gradient Boosting with Temporal Data (2018)Predicting Customer Churn: Ex…Digitalisation and Big Data Mining in Banking (2018)Digitalisation and Big Data M…Behavioral attributes and financial churn prediction (2018)Behavioral attributes and fin…A big data analytics model for customer churn prediction in the retiree segment (2018)A big data analytics model fo…Comparison of Deep Learning Algorithms to Predict Customer Churn within a Local Retail Industry (2017)Comparison of Deep Learning A…Churn prediction on huge telecom data using hybrid firefly based classification (2017)Churn prediction on huge tele…An efficient system for customer churn prediction through particle swarm optimization based feature selection model with simulated annealing (2017)An efficient system for custo…Predicting Customer Churn in Banking Industry using Neural Networks (2016)Predicting Customer Churn in …Developing a prediction model for customer churn from electronic banking services using data mining (2016)Developing a prediction model…Identification of Customer Churn Considering Difficult Case Mining (2023)Identification of Customer Ch…Bankruptcy prediction using neural networks (1994)Applying data mining to telecom churn management (2005)Methodologies for data quality assessment and improvement (2009)Customer Churn Analysis and Prediction Using Data Mining Models in Banking Industry (2019)Experimental Analysis of Hyperparameters for Deep Learning-Based Churn Prediction in the Banking Sector (2021)Towards Explainable Machine Learning for Bank Churn Prediction Using Data Balancing and Ensemble-Based Methods (2022)Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting (2004)Building comprehensible customer churn prediction models with advanced rule 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Model and Flow Theory to Online Consumer Behavior (2002)Business Intelligence and Analytics: From Big Data to Big Impact (2012)Beyond the hype: Big data concepts, methods, and analytics (2014)Big data analytics for financial Market volatility forecast based on support vector machine (2019)Bridging marketing theory and big data analytics: The taxonomy of marketing attribution (2020)Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis (2021)Understanding the Customer Base of Service Providers: An Examination of the Differences between Switchers and Stayers (2000)A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees (2018)Customer churn prediction in telecom using machine learning in big data platform (2019)Data-Driven Decision-Making: Accurate Customer Churn Prediction with Cat-Boost (2024)Novel embedding model predicting the credit card's 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this work references cited by second hop

References

  • 2018Digitalisation and Big Data Mining in Banking132 cited
  • 2018A big data analytics model for customer churn prediction in the retiree segment125 cited
  • 2016Developing a prediction model for customer churn from electronic banking services using data mining116 cited
  • 2017Churn prediction on huge telecom data using hybrid firefly based classification87 cited
  • 2016Predicting Customer Churn in Banking Industry using Neural Networks78 cited
  • 2017An efficient system for customer churn prediction through particle swarm optimization based feature selection model with simulated annealing63 cited
  • 2017Comparison of Deep Learning Algorithms to Predict Customer Churn within a Local Retail Industry51 cited
  • 2018Behavioral attributes and financial churn prediction49 cited
  • 2018Predicting Customer Churn: Extreme Gradient Boosting with Temporal Data15 cited

Cited by

  • 2023Identification of Customer Churn Considering Difficult Case Mining11 cited

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