Hybrid Prediction Model for the Success of Bank Telemarketing

Desai, Rohan; Khairnar, Vaishali D.

doi:10.1007/978-981-16-2422-3_54

Cited by 3 publications

(3 citation statements)

References 21 publications

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“…Borugadda et al (2021) used various machine learning algorithms such as Random Forest (RF), Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), Decision Tree (DT) and Logistic Regression (LR) to explore the demand for long-term bank deposits. Desai and Khairnar (2022) propose a hybrid model based on artificial neural network and extreme gradient boosting by examining the comprehensive features associated with a Portuguese bank's customers, products and services. The proposed model was compared with known machine learning methods and successful results were obtained.…”

Section: Background Of the Studymentioning

confidence: 99%

Machine Learning Approach and Model Performance Evaluation for Tele-Marketing Success Classification

Koçoğlu

Esnaf

2022

International Journal of Business Analytics

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Up to the present, various methods such as Data Mining, Machine Learning, and Artificial Intelligence have been used to get the best assess from huge and important data resource. Deep Learning, one of these methods, is extended version of Artificial Neural Networks. Within the scope of this study, a model has been developed to classify the success of tele-marketing with different machine learning algorithms especially with Deep Learning algorithm. Naïve Bayes, C5.0, Extreme Learning Machine and Deep Learning algorithms have been used for modelling. To examine the effect of class label distribution on model success, Synthetic Minority Oversampling Technique have been used. The results have revealed the success of Deep Learning and Decision Trees algorithms. When the data set was not balanced, the Deep Learning algorithm performed better in terms of sensitivity. Among all models, the best performance in terms of accuracy, precision and F-score have been achieved with the C5.0 algorithm.

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Section: Background Of the Studymentioning

confidence: 99%

Machine Learning Approach and Model Performance Evaluation for Tele-Marketing Success Classification

Koçoğlu

Esnaf

2022

International Journal of Business Analytics

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“…As is known, telemarketing is defined as a system (or technique) for direct marketing in which the client is contacted in different ways to avail or purchase a product or facility 18 . The specific case of this action for banking subjects is called bank telemarketing 19 .…”

Section: Introductionmentioning

confidence: 99%

“…Utilizing a deep convolutional ANN, Kim et al 32 achieved 71% accuracy of prediction for the same purpose (i.e., predicting the success rate of telemarketing calls). Likewise, Desai and Khairnar 18 used a hybrid of ANN and extreme gradient boosting applied to the telemarketing of a Portuguese bank that was affected by an economic crisis. The suggested model outperformed different typical machine learning models.…”

Section: Introductionmentioning

confidence: 99%

A novel ensemble approach for estimating the competency of bank telemarketing

Guo,

Yao,

Liu

et al. 2023

Sci Rep

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Having a reliable understanding of bank telemarketing performance is of great importance in the modern world of economy. Recently, machine learning models have obtained high attention for this purpose. In order to introduce and evaluate cutting-edge models, this study develops sophisticated hybrid models for estimating the success rate of bank telemarketing. A large free dataset is used which lists the clients’ information of a Portuguese bank. The data are analyzed by four artificial neural networks (ANNs) trained by metaheuristic algorithms, namely electromagnetic field optimization (EFO), future search algorithm (FSA), harmony search algorithm (HSA), and social ski-driver (SSD). The models predict the subscription of clients for a long-term deposit by evaluating nineteen conditioning parameters. The results first indicated the high potential of all four models in analyzing and predicting the subscription pattern, thereby, revealing the competency of neuro-metaheuristic hybrids. However, comparatively speaking, the EFO yielded the most reliable approximation with an area under the curve (AUC) around 0.80. FSA-ANN emerged as the second-accurate model followed by the SSD and HSA with respective AUCs of 0.7714, 0.7663, and 0.7160. Moreover, the superiority of the EFO-ANN is confirmed against several conventional models from the previous literature, and finally, it is introduced as an effective model to be practically used by banking institutions for predicting the likelihood of deposit subscriptions.

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Comparative explainable Machine learning to evaluate Bank marketing success

Vashi,

Shanbhag

2023

2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)

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Hybrid Prediction Model for the Success of Bank Telemarketing

Cited by 3 publications

References 21 publications

Machine Learning Approach and Model Performance Evaluation for Tele-Marketing Success Classification

Machine Learning Approach and Model Performance Evaluation for Tele-Marketing Success Classification

A novel ensemble approach for estimating the competency of bank telemarketing

Comparative explainable Machine learning to evaluate Bank marketing success

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