Accuracy rate of predictive models in credit screening

Suebsing, Anirut; Vajiramedhin, Chakarin

doi:10.12988/ams.2013.38457

Cited by 2 publications

(1 citation statement)

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“…To understand customer behavior, many banks have adopted the predictive technique based on the data mining to predict the customer data for classifying the customers before offering special services. The prediction or classification is the most important task in the data mining that is usually applied to classify the group of data [2]. Thus, many predictive models ( [3], [4], [6], [7], [9], [10]) have been proposed that each model has its own advantages and disadvantages vary.…”

Section: Introductionmentioning

confidence: 99%

Feature selection with data balancing for prediction of bank telemarketing

Vajiramedhin¹,

Suebsing²

2014

ams

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Nowadays, Telemarketing is an interactive technique of direct marketing that many banks apply to present a long term deposit to bank customers via the phone. Although the offering like this manner is powerful, it may make the customers annoyed. The data prediction is a popular task in data mining because it can be applied to solve this problem. However, the predictive performance may be decreased in case of the input data have many features like the bank customer information. In this paper, we focus on how to reduce the feature of input data and balance the training set for the predictive model to help the bank to increase the prediction rate. In the system performance evaluation, all accuracy rates of each predictive model based on the proposed approach compared with the original predictive model based on the truth positive and receiver operating characteristic measurement show the high performance in which the smaller number of features.

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Section: Introductionmentioning

confidence: 99%