Customer Churning Prediction Using Support Vector Machines in Online Auto Insurance Service

Hur, Yeon; Lim, Sehun

doi:10.1007/11427445_149

Cited by 15 publications

(6 citation statements)

References 8 publications

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“…SVM is an extension of the support vector classifier that results from enlarging the feature space in a specific way; using kernel the algorithm tries to find the optimal hyperplane which can be used to classify new data points. In churn prediction analysis Hur et al (2005) find SVM outperforms other learning methods such as Decision Tree and Artificial Neural Network.…”

Section: 2mentioning

confidence: 98%

Bank Customer Churn Prediction Using Machine Learning Framework

Ashraf

2024

Journal of Applied Finance &Amp; Banking

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Using real customer data from a large community bank in the South of the US, this paper analyzes the customer churn prediction problem by constructing and comparing ten machine learning classification models with five sample techniques. Our results show that Random Forest, XG Boost, AdaBoost, and Bagging Meta classifiers dominate others in terms of overall accuracy, F-score, and AUC curve for the test observations. For the four classifiers, the overall accuracy ranges from 87% to 96% across five different sampling methods explored, while the AUC values range between 0.9 to 0.93. Considering overall accuracy and F-Score, AdaBoost with original and MTDF sampling technique dominates others; however, considering the AUC measure, XG Boost and Random Forest perform similarly to AdaBoost, which slightly dominate Bagging Meta across all sampling techniques; although the performance measures for these four classifiers are comparable across all sampling techniques. The paper further presents important features of customer churn behavior as predicted by the model. The diagnostic analysis also provides an insightful comparison between churned and non-churned customers. JEL classification numbers: C0, C5, C8, G21. Keywords: Machine learning, Big data, Sampling techniques, Customer churn, Customer retention, Financial services, Community bank.

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Section: 2mentioning

confidence: 98%

Bank Customer Churn Prediction Using Machine Learning Framework

Ashraf

2024

Journal of Applied Finance &Amp; Banking

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“…A popular method for improving the performance of SVMs is the utilization of kernel functions [8]. In addressing customer churn problems, SVM may exhibit superior performance in comparison to Artificial Neural Networks (ANNs) and Decision Trees (DTs) based on the specific characteristics of the data [17,30].…”

Section: B Support Vector Machinementioning

confidence: 99%

Hyperparameter Optimization and Combined Data Sampling Techniques in Machine Learning for Customer Churn Prediction: A Comparative Analysis

Imani,

Arabnia

2023

Preprint

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In this paper, a variety of machine learning techniques, including Artificial Neural Networks, Decision Trees, Support Vector Machines, Random Forests, Logistic Regression, and three gradient boosting techniques (XGBoost, LightGBM, and CatBoost), were employed to predict customer churn in the telecommunications industry using a publicly available dataset. To address the issue of imbalanced data, various data sampling techniques, such as SMOTE, the combination of SMOTE with Tomek Links, and the combination of SMOTE with Edited Nearest Neighbors, were implemented. Additionally, hyperparameter tuning was utilized to optimize the performance of the machine learning models. The models were evaluated and compared using commonly used metrics, including Precision, Recall, F1-Score, and the Receiver Operating Characteristic Area Under Curve (ROC AUC). The results revealed that the performance of the models was enhanced by the application of hyperparameter tuning and the combined data sampling methods on the training data. Overall, LightGBM demonstrated superior performance compared to the other machine learning techniques examined. The findings indicate that LightGBM exhibited a superior performance both prior to and following the application of these techniques.

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“…In financial services (banking and insurance), churn is usually seen as closing accounts [32]. [36] predict the switching probability of an insured person to another auto insurance company. As far as retail is concerned, most studies also focus on the customer's ability to leave to identify the exact moment when customers will discontinue their relationship with companies.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning for Customer Churn Prediction in E-Commerce Decision Support

et al. 2021

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Churn prediction is a Big Data domain, one of the most demanding use cases of recent time. It is also one of the most critical indicators of a healthy and growing business, irrespective of the size or channel of sales. This paper aims to develop a deep learning model for customers’ churn prediction in e-commerce, which is the main contribution of the article. The experiment was performed over real e-commerce data where 75% of buyers are one-off customers. The prediction based on this business specificity (many one-off customers and very few regular ones) is extremely challenging and, in a natural way, must be inaccurate to a certain ex-tent. Looking from another perspective, correct prediction and subsequent actions resulting in a higher customer retention are very attractive for overall business performance. In such a case, predictions with 74% accuracy, 78% precision, and 68% recall are very promising. Also, the paper fills a research gap and contrib-utes to the existing literature in the area of developing a customer churn prediction method for the retail sector by using deep learning tools based on customer churn and the full history of each customer’s transactions.

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Customer Churning Prediction Using Support Vector Machines in Online Auto Insurance Service

Cited by 15 publications

References 8 publications

Bank Customer Churn Prediction Using Machine Learning Framework

Bank Customer Churn Prediction Using Machine Learning Framework

Hyperparameter Optimization and Combined Data Sampling Techniques in Machine Learning for Customer Churn Prediction: A Comparative Analysis

Deep Learning for Customer Churn Prediction in E-Commerce Decision Support

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