Application of Machine Learning and Statistics in Banking Customer Churn Prediction

Shukla, Animesh

doi:10.1109/icscc51209.2021.9528258

Cited by 4 publications

(2 citation statements)

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“…In recent years, there have been countless applications of machine learning [ 19 ] and reinforcement learning [ 20 ] in the diversified areas such as healthcare predictions [ 21 ], cloud resource management [ 22 ], and mobile robot navigation [ 23 ]. Moreover, a significant surge is also observed in cyber frauds, as well as the corresponding model to counter them, such as credit card fraud detection, telecom churn prediction [ 2 – 5 ], and detecting rare medical diseases. In the models mentioned above, classifiers are trained to handle most costly errors compared to others.…”

Section: Related Workmentioning

confidence: 99%

“…A few reasons for churn are dissatisfaction in services such as unattractive recharge plans, frequent call drops, insufficient bandwidth, frequent customer care calls, unreachable networks, and slow Internet speed. In general, several techniques are used to address the customer churn prediction such as statistical learning [ 2 ], machine learning [ 3 ], evolutionary optimization technique [ 4 ], and deep learning [ 5 ]. Boosting is an ensemble technique that attempts to create a robust classifier from several weak classifiers.…”

Section: Introductionmentioning

confidence: 99%

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Clairvoyant: AdaBoost with Cost-Enabled Cost-Sensitive Classifier for Customer Churn Prediction

Thakkar

Desai

Ghosh³

et al. 2022

Computational Intelligence and Neuroscience

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Customer churn prediction is one of the challenging problems and paramount concerns for telecommunication industries. With the increasing number of mobile operators, users can switch from one mobile operator to another if they are unsatisfied with the service. Marketing literature states that it costs 5–10 times more to acquire a new customer than retain an existing one. Hence, effective customer churn management has become a crucial demand for mobile communication operators. Researchers have proposed several classifiers and boosting methods to control customer churn rate, including deep learning (DL) algorithms. However, conventional classification algorithms follow an error-based framework that focuses on improving the classifier’s accuracy over cost sensitization. Typical classification algorithms treat misclassification errors equally, which is not applicable in practice. On the contrary, DL algorithms are computationally expensive as well as time-consuming. In this paper, a novel class-dependent cost-sensitive boosting algorithm called AdaBoostWithCost is proposed to reduce the churn cost. This study demonstrates the empirical evaluation of the proposed AdaBoostWithCost algorithm, which consistently outperforms the discrete AdaBoost algorithm concerning telecom churn prediction. The key focus of the AdaBoostWithCost classifier is to reduce false-negative error and the misclassification cost more significantly than the AdaBoost.

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