Long-term deposits prediction: a comparative framework of classification model for predict the success of bank telemarketing

Ilham, Ahmad; Khikmah, Laelatul; Ulumuddin,; Iswara, Ida Bagus Ary Indra

doi:10.1088/1742-6596/1175/1/012035

Cited by 8 publications

(4 citation statements)

References 7 publications

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“…The results of the research also highlighted that Neural Networks reached the highest performance of all the other applied techniques. However, a recent research by (Ilham, Khikmah, Indra, Ulumuddin, & Iswara, 2019) contradicted this conclusion by applying a set of classification techniques over banking customers' data. The set included Neural Networks, K-Nearest Neighbor, Random Forest, and others, then it concluded the higher performance of the Support Vector Machine over all the presented algorithms.…”

Section: Applications Of Data Mining In Bankingmentioning

confidence: 99%

A Collaborative Mining-Based Decision Support Model for Granting Personal Loans in the Banking Sector

Idrees

Khedr

2022

International Journal of E-Services and Mobile Applications

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One main potential objective for financial corporations is to retain long-term customers. Configuring customer knowledge is no doubt mandatory to lower the risk level. Loans and credit cards granting are two services that are offered by the banking corporations which can be categorized as high-risk services. Therefore, it is highly recommended for the corporations to have intelligent support for providing an accurate granting decision which naturally leads to minimizing the associated risk. In this research, a decision support model is proposed for loans granting. The proposed model applies a set of data mining techniques in a collaborative environment that aims at applying different techniques with considering their results according to the technique’s evaluation weight. The proposed model results present the recommendation for each customer’s loan granting a request to be either accepted or rejected. The proposed approach has been applied the on a loan granting dataset and the evaluation results revealed its superiority by 92% success in reaching high accurate decisions.

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Section: Applications Of Data Mining In Bankingmentioning

confidence: 99%

A Collaborative Mining-Based Decision Support Model for Granting Personal Loans in the Banking Sector

Idrees

Khedr

2022

International Journal of E-Services and Mobile Applications

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“…Banking industries have used data mining techniques in various applications, especially on bank failure prediction [1][2][3], possible bank customer churns identification [4], fraudulent transaction detection [5], customer segmentation [8][9][10], predictions on bank telemarketing [11][12][13][14], and sentiment analysis for bank customers [15]. Some of the classification studies in the banking sector have been compared in Table 1.…”

Section: Related Workmentioning

confidence: 99%

“…Apart from novel task-specific algorithms proposed by the authors, the most commonly used classification algorithms in the banking sector are decision tree (DT), neural network (NN), support vector machine (SVM), k-nearest neighbor (KNN), Naive Bayes (NB), and logistic regression (LR), as shown in Table 1. Some data mining studies in the banking sector [1,2,6,11,15] have used ensemble learning methods to increase the classification performance. Bagging and boosting are the most popular ensemble learning methods due to their theoretical performance advantages.…”

Section: Related Workmentioning

confidence: 99%

“…Bagging and boosting are the most popular ensemble learning methods due to their theoretical performance advantages. Random forest (RF) [2,6,11,15], AdaBoost (AB) [6], and extreme gradient boosting (XGB) [2,15] have also been used in the banking sector as the most well-known bagging and boosting algorithms, respectively. As shown in Table 1, accuracy (ACC) and area under ROC curve (AUC) are the commonly used performance measures for classification.…”

Section: Related Workmentioning

confidence: 99%

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Data Mining in Banking Sector Using Weighted Decision Jungle Method

Birant¹

2021

Data Mining - Methods, Applications and Systems

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Classification, as one of the most popular data mining techniques, has been used in the banking sector for different purposes, for example, for bank customer churn prediction, credit approval, fraud detection, bank failure estimation, and bank telemarketing prediction. However, traditional classification algorithms do not take into account the class distribution, which results into undesirable performance on imbalanced banking data. To solve this problem, this paper proposes an approach which improves the decision jungle (DJ) method with a class-based weighting mechanism. The experiments conducted on 17 real-world bank datasets show that the proposed approach outperforms the decision jungle method when handling imbalanced banking data.

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Application of Decision Tree Algorithm for the Classification Problem in Bank Telemarketing

Lam,

Tran,

Dinh

2023

Lecture Notes in Networks and Systems

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Long-term deposits prediction: a comparative framework of classification model for predict the success of bank telemarketing

Cited by 8 publications

References 7 publications

A Collaborative Mining-Based Decision Support Model for Granting Personal Loans in the Banking Sector

A Collaborative Mining-Based Decision Support Model for Granting Personal Loans in the Banking Sector

Data Mining in Banking Sector Using Weighted Decision Jungle Method

Application of Decision Tree Algorithm for the Classification Problem in Bank Telemarketing

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