使用机器学习和自动化预测信用卡客户流失

R. Gupta, S. Bharti, Nikhlesh Pathik, Ashutosh Sharma
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引用次数: 0

摘要

如今,大多数零售银行主要担心的是客户波动带来的风险,这种风险增加了几乎所有金融产品的成本。在这项工作中,作者比较了不同的方法和算法来预测影响客户流失的相关特征,这意味着我们可以找到减少客户流失和创造普惠金融的方法。本研究采用了不同的机器学习技术,如决策树分类器、随机森林分类器、AdaBoost分类器、极端梯度增强以及随机欠采样和随机过采样平衡数据。作者还实现了AutoML来进一步比较不同的模型,并提高模型预测客户流失的准确性。结果表明,采用AutoML最高准确率模型,在处理能力较低的情况下,决策树分类器的准确率为93.48%,而采用AutoML最高准确率模型的准确率为97.53%。重要的特征是“总交易金额”和“总交易计数”,以预测给定数据集的客户流失。
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Predicting Churn of Credit Card Customers Using Machine Learning and AutoML
Nowadays, a major concern for most retail banks is the risk that originates from customer fluctuation and that increases the cost of almost every financial product. In this work, the authors compared different approaches and algorithms to predict the relevant features that affect the customer churn, which means we can find ways to reduce the customer churn and create financial inclusion. This research was conducted by applying different machine learning techniques like decision tree classifier, random forest classifier, AdaBoost classifier, extreme gradient boosting, and balancing data with random under-sampling and random oversampling. The authors have also implemented AutoML to further compare different models and improve the accuracy of the model to predict customer churn. It was observed that applying AutoML highest accuracy model gave the accuracy of 97.53% in comparison to that of the decision tree classifier, which was 93.48% with the use of low processing power. Important features were ‘total transaction amount' and ‘total transaction count' to predict customer churn for a given dataset.
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