Churn Prediction in Banking System using K-Means, LOF, and CBLOF

Ullah, Irfan; Hussain, Hameed; Ali, Iftikhar; Liaquat, Anum

doi:10.1109/icecce47252.2019.8940667

Cited by 11 publications

(13 citation statements)

References 6 publications

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“…Jafari-Marandi et al [121] explored a similar approach that combine clustering methods parallel to classification methods with the aim of creating more control in the decision-making process of churn management, but at the least, expect to increase the accuracy by exploring the individuality of each customer to optimize the classification decision process. Ullah et al [107] also employed clustering by employing fuzzy c-means, possibility c-means and possibility fuzzy c-means. Other studies applied k-means [106,107], local outlier factors [107], and cluster based local outlier factors [107] for classification.…”

Section: B Rq2 -What Algorithms Have Been Employed To Predict Dropout?mentioning

confidence: 99%

“…Ullah et al [107] also employed clustering by employing fuzzy c-means, possibility c-means and possibility fuzzy c-means. Other studies applied k-means [106,107], local outlier factors [107], and cluster based local outlier factors [107] for classification. Support vector machines are algorithms that attempt to obtain an optimal hyperplane to maximize the margin between positive examples and negative examples [32].…”

Section: B Rq2 -What Algorithms Have Been Employed To Predict Dropout?mentioning

confidence: 99%

“…Al-Molhem et al [68] conducted social network analysis to enhance the results of churn prediction models in the telecom domain using call detail records to construct a weighed graph representing the distance between two subscribers to calculate the centrality. The usage of marketing related variables, such as promotions offered to a customer, calls developed in a retention strategy, and helpdesk interactions, were applied by Verbeke et al [50] However, certain studies did not identify the features employed [9,12,18,22,25,42,48,67,70,73,85,87,88,97,98,99,107,110,112,113,116,119,122,127], which in some cases are related to the usage of more than one database. Idris et al [119] used two databases (orange and cell2cell).…”

Section: Rq3 -What Are the Features Used To Predict Dropout?mentioning

confidence: 99%

“…Sensitivity appears in several studies, and its higher adoption could be related to the importance of correctly predicting the customers who will drop out, i.e., the proportion of dropouts that truly dropout. This metric was referred to as sensitivity in certain studies [41,41,50,85,101,103,104,106,108,109,116,117,119], recall [26,45,69,72,75,76,105,105,107,111,114,121], or true positive [12,25,77,82,102,111,112,113,115].…”

Section: E Rq5 -How Is Machine Learning Algorithm Dropout Prediction Accuracy Measured?mentioning

confidence: 99%

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A SLR on Customer Dropout Prediction

et al. 2022

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Dropout prediction is a problem that is being addressed with machine learning algorithms; thus, appropriate approaches to address the dropout rate are needed. The selection of an algorithm to predict the dropout rate is only one problem to be addressed. Other aspects should also be considered, such as which features should be selected and how to measure accuracy while considering whether the features are appropriate according to the business context in which they are employed. To solve these questions, the goal of this paper is to develop a systematic literature review to evaluate the development of existing studies and to predict the dropout rate in contractual settings using machine learning to identify current trends and research opportunities. The results of this study identify trends in the use of machine learning algorithms in different business areas and in the adoption of machine learning algorithms, including which metrics are being adopted and what features are being applied. Finally, some research opportunities and gaps that could be explored in future research are presented.

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Section: B Rq2 -What Algorithms Have Been Employed To Predict Dropout?mentioning

confidence: 99%

Section: B Rq2 -What Algorithms Have Been Employed To Predict Dropout?mentioning

confidence: 99%

Section: Rq3 -What Are the Features Used To Predict Dropout?mentioning

confidence: 99%

Section: E Rq5 -How Is Machine Learning Algorithm Dropout Prediction Accuracy Measured?mentioning

confidence: 99%

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A SLR on Customer Dropout Prediction

et al. 2022

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“…In Figure 7, the (CBLOF) algorithm [33] is combined with the K-Means and BIRCH [34] algorithms. CBLOF assigns an anomaly score to the clustered data by first classifying each cluster as small or large using parameters alpha and beta.…”

Section: Cblof (K-means or Birch)-detectormentioning

confidence: 99%

Combining Artificial Immune System and Clustering Analysis: A Stock Market Anomaly Detection Model

Close¹,

Kashef²

2020

JILSA

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Artificial intelligence research in the stock market sector has been heavily geared towards stock price prediction rather than stock price manipulation. As online trading systems have increased the amount of high volume and real-time data transactions, the stock market has increased vulnerability to attacks. This paper aims to detect these attacks based on normal trade behavior using an Artificial Immune System (AIS) approach combined with one of four clustering algorithms. The AIS approach is inspired by its proven ability to handle time-series data and its ability to detect abnormal behavior while only being trained on regular trade behavior. These two main points are essential as the models need to adapt over time to adjust to normal trade behavior as it evolves, and due to confidentiality and data restrictions, real-world manipulations are not available for training. This paper discovers a competitive alternative to the leading approach and investigates the effects of combining AIS with clustering algorithms; Kernel Density Estimation, Self-Organized Maps, Density-Based Spatial Clustering of Applications with Noise and Spectral clustering. The best performing solution achieves leading performance using common clustering metrics, including Area Under the Curve, False Alarm Rate, False Negative Rate, and Computation Time.

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Machine Learning and Neural Network Models for Customer Churn Prediction in Banking and Telecom Sectors

Patil

Danve

et al. 2022

Proceedings of Second International Conference on Advances in Computer Engineering and Communication Systems

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Churn Prediction in Banking System using K-Means, LOF, and CBLOF

Cited by 11 publications

References 6 publications

A SLR on Customer Dropout Prediction

A SLR on Customer Dropout Prediction

Combining Artificial Immune System and Clustering Analysis: A Stock Market Anomaly Detection Model

Machine Learning and Neural Network Models for Customer Churn Prediction in Banking and Telecom Sectors

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