Bank Customer Churn Research Articles

Research on Integrated Learning Algorithm Model of Bank Customer Churn Prediction

With the rapid growth of Internet finance, competition within the banking industry has intensified significantly. To better understand customer needs and enhance customer loyalty, it has become crucial to develop a customer churn prediction model. Such a model enables banks to identify customers at risk of leaving, support data-driven business decisions, and implement strategies to retain valuable clients, thereby safeguarding the bank's interests. In this context, this paper presents a customer churn prediction model based on an ensemble learning algorithm. Experimental results demonstrate that the model effectively predicts and analyzes potential customer churn, providing valuable insights for retention efforts.

Bank Customer Churn Prediction with Machine Learning Methods

This paper examines and analyses customer churn prediction in the banking sector using the data from ABC Bank. The analysis conducted will document the determinants of bank customer churn and provide insights to the most important factors which influence the customers decision to quit utilizing the services of a bank. The investigation is based on the results of two machine learning algorithms with k-fold-cross-validation and same boosting methods. The result of the analysis reveals that out of logistic regression and random forests algorithms, the random forest methods show a higher accuracy score which corresponds with the literature review studied. Furthermore, the statistic of the research indicates that customers age has the highest association with the likelihood of customer churning, while whether the customer has a credit card at the bank has the lowest interconnection. The results of this research may provide valid explanations to customer churn in the banking sector and bring further intuitions of the advantages which machine learning methods may provide to future financial analysis.

The causes of bank customer churn based on XGBoost and LightGBMmodels: the evidence from the Kaggle dataset

As the digital economy continues to grow, the expansion of Internet finance introduces new challenges for the conventional banking industry. Banks must deal with multiple pressures, such as digital transformation, declining customer loyalty, and fintech competition. Analyzing the potential factors of bank customer churn from multiple perspectives and constructing models for predicting churn can help bank managers understand the causes of churn, identify problems, detect potential churn customers promptly, and develop efficient retention strategies based on customer characteristics and preferences. In this paper, we used a combination of visualization, data mining, and machine learning methods to analyze the factors used to predict bank customer churn from multiple perspectives, such as feature selection (Random Forest Feature Importance Ranking), feature extraction (PCA), visualization, etc. We also constructed two churn prediction models based on the gradient boosting tree algorithms, XGBoost and LightGBM, compared the evaluation measures before and after feature selection and before and after tuning parameters, and interpreted the model through SHAP methods. After the paper, the following conclusions were drawn: (1) Total Trans Amt, Total Trans Ct, and Total Revolving Bal are pivotal in analyzing and predicting customer churn; (2) the SHAP Summary Plot can react to the visual analysis of predictors of customer churn to a certain extent; (3) the effect of feature selection on the assessment of the results is sometimes insignificant; (4) tuning parameter settings can enhance model performance to a certain extent, but the optimal parameters may vary based on the preprocessing method employed. These conclusions will assist banks in comprehending customer churn factors more deeply, constructing a higher performance churn prediction model, and conducting a comprehensive result synthesis analysis.

Explaining and predicting customer churn by monotonic rules induced from ordinal data

In the course of a computational experiment on bank customer churn data, we demonstrate the explanatory and predictive capacity of monotonic decision rules. The data exhibit a partially ordinal character, as certain attribute value sets describing the clients are ordered and demonstrate a monotonic relationship with churn or non-churn outcomes. The data are structured by the Variable Consistency Dominance-based Rough Set Approach (VC-DRSA) prior to the induction of monotonic decision rules. The supervised learning is conducted using an extended version of VC-DRSA, implemented in RuLeStudio and RuleVisualization programs. The first one is designed to experiment with parameterized rule models, and the second one is used for visualization and a thorough examination of the rule model. The monotonic decision rules give insight into the bank data, characterizing loyal customers and the ones who left the bank. Such an approach is in line with explainable AI, aiming to obtain a transparent decision model, that can be easily understood by decision-makers. We also compare the predictive performance of monotonic rules with some well-known machine learning models.

Bank Customer Churn Based on Different Models, Oversampling, and Encoding Methods

Customer churn prediction (CCP) is one of the cornerstones of Customer Relationship Management (CRM), in which one seeks to forecast whether or not a customer will quit the organization. Currently, plenty of algorithmic focuses on CCP. To fill the gap in the current study, this paper builds different models to predict bank user churn based on data from Kaggle. Specifically, we investigate the difference between models with and without oversampling, as well as discuss the difference between models under different coding methods. According to the results, ‘smote’ does not necessarily improve the performance accuracy, one hot encoding is more effective than target encoding. Finally, after all aspects of comparison, the logistic regression model is more reliable in the future analysis of customer churn of commercial banks. These results offer a guideline for future bank customer churn prediction.

Prediction and Clustering of Bank Customer Churn Based on XGBoost and K-means

Due to the fierce competition of commercial banks, customers are becoming more and more important to banks. Therefore, customer churn has become a major problem that banks need to face. In this paper, XGboost algorithm was used on a data set of customers of a US bank from Kaggle to predict customer churn, and grid search method was used to find the best hyperparameters. Moreover, K-means algorithm is adopted to further subdivide the lost customers. For predicting customer churn, XGBoost algorithm achieves 0.84 in accuracy, 0.83 in precision, 0.84 in recall and 0.84 in F1 score on the test set. And the most important score for features in the case of the algorithm adopted are customers' estimated salary, credit score and balance. For the segmentation of churn customers, K-means algorithm divides these customers into 5 groups. These five groups of customers have different values for banks, so this paper puts forward corresponding recovery suggestions for their respective characteristics

Case-Based Teaching for Python Language Under the Background of Big Data

This paper proposes a new teaching method for the Python language programming course, which can better enable students to understand and use in the background of big data. Python is an open-source programming language with a community-based model. In this paper, firstly, various functions are described in Python Language. Additionally, different application areas are presented in this paper, such as transportation logistics, urban management, biomedical field, smart power grid, energy field, and commercial field. Finally, bank customer churn as case-based teaching is introduced can improve the students’ confidence in their future studies.

WITHDRAWN: REHC: Reduction through exclusive homogeneous clusters for imbalance dataset

Abstract Customer retention is a challenging issue of Customer Relationship Management (CRM). To retain a customer it is necessary to predict the customer churn earlier. Retail banking also facing this critical customer churn. Analyzing the customers’ demographic attributes and their behavioral economics enables to predict bank customer churn. The transaction history of customers’ transaction has large data records. Data Reduction on huge volumes of data is certainly required for effective prediction of bank customer churn. This research explores the data reduction techniques that enhances the feature extraction from time series data and further helps for better prediction of customer attrition in banking sector. The proposed approach REHC is helpful to reduce dataset which reflects on time consumption in training the imbalanced dataset and manage CRM effectively by predicting customer churn. It is approached through k-means clustering technique.

Study on the Prediction of Imbalanced Bank Customer Churn Based on Generative Adversarial Network

The imbalanced commercial bank customer data will lead to the unpredictability of the minority class. Therefore, this paper proposes an imbalanced data method based on generative adversarial network to deal the problem of poor prediction performance of traditional classifiers for minority class. This paper method is based on the generative adversarial network to generate minority class samples to improve imbalanced data. Finally, the classifier is used to train the balanced data to improve the prediction performance of minority class. In this experiment, the data of a commercial bank customer were measured with indicators such as F1, Precision, and compared with traditional data sampling methods such as SMOTE, BSSMOTE. This method is feasible and applicable to the classification of imbalanced data of banks by observing the experimental results, which has better application value.

On the build and application of bank customer churn warning model

In view of the customer churn problem faced by banks, this paper will use the Python language to clean and select the original dataset based on real bank customer data, and gradually condense the 626 customer features in the original dataset to 77 customer features. Then, based on the pre-processed bank data, this paper uses logistic regression, decision tree and neural network to establish three bank customer churn warning models and compares them. The results show that the accuracy of the three models in predicting bank loss customers is above 92%. Finally, based on the logistic regression model with better evaluation results, this paper analyses the characteristics of the lost customers for the bank, and gives the bank management suggestions for the lost customers.

On the build and application of bank customer churn warning model

In view of the customer churn problem faced by banks, this paper will use the Python language to clean and select the original dataset based on real bank customer data, and gradually condense the 626 customer features in the original dataset to 77 customer features. Then, based on the pre-processed bank data, this paper uses logistic regression, decision tree and neural network to establish three bank customer churn warning models and compares them. The results show that the accuracy of the three models in predicting bank loss customers is above 92%. Finally, based on the logistic regression model with better evaluation results, this paper analyses the characteristics of the lost customers for the bank, and gives the bank management suggestions for the lost customers.

Applied Research on Data Mining in Bank Customer Churn

With the increasing competition in the banking sector, banks make every effort to compete for valuable customer resources in order to get more benefits, but how to make customers to retain, or reduce the loss of customers is become the key issues for the banks. In this paper, using theoretical research and practical method to discusses how to use data mining techniques to predict the bank's customer churn. Identifying risky customers and taking certain measures before customers really lost .The research will provide a reliable basis for decision-making of banks.

Customer Churn Prediction Based on Feature Clustering and Nonparallel Support Vector Machine

Bank customer churn prediction is one of the key businesses for modern commercial banks. Recently, various methods have been investigated to identify the customers who would leave away. This paper proposed a new framework based on feature clustering and classification technique to help commercial banks make an effective decision on customer churn problem. The proposed method benefits from the result of data explorations, clusters the customer features, and makes a decision with a state-of-the-art classifier. When facing the data with large amount of missing items, it does not directly remove the features by some subjective threshold, but clusters the features through the consideration of the relationship and the missing ratio. Real-world data from a major commercial bank of China verifies the feasibility of our framework in industrial applications.

Prediction of Customer Attrition of Commercial Banks based on SVM Model

Currently, Chinese commercial banks are facing triple tremendous pressure, including financial disintermediation, interest rate marketization and Internet finance. Meanwhile, increasing financial consumption demand of customers further intensifies the competition among commercial banks. To increase their profits for continuing operations and enhance the core competitiveness, commercial banks must avoid the loss of customers while acquiring new customers. This paper discusses commercial bank customer churn prediction based on SVM model, and uses random sampling method to improve SVM model, considering the imbalance characteristics of customer data sets. The results show that this method can effectively enhance the prediction accuracy of the selected model.

Customer churn prediction using improved balanced random forests

Churn prediction is becoming a major focus of banks in China who wish to retain customers by satisfying their needs under resource constraints. In churn prediction, an important yet challenging problem is the imbalance in the data distribution. In this paper, we propose a novel learning method, called improved balanced random forests (IBRF), and demonstrate its application to churn prediction. We investigate the effectiveness of the standard random forests approach in predicting customer churn, while also integrating sampling techniques and cost-sensitive learning into the approach to achieve a better performance than most existing algorithms. The nature of IBRF is that the best features are iteratively learned by altering the class distribution and by putting higher penalties on misclassification of the minority class. We apply the method to a real bank customer churn data set. It is found to improve prediction accuracy significantly compared with other algorithms, such as artificial neural networks, decision trees, and class-weighted core support vector machines (CWC-SVM). Moreover, IBRF also produces better prediction results than other random forests algorithms such as balanced random forests and weighted random forests.