Business Analytics using Random Forest Trees for Credit Risk Prediction: A Comparison Study

Ghatasheh, Nazeeh

doi:10.14257/ijast.2014.72.02

Cited by 50 publications

(20 citation statements)

References 16 publications

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“…Data-driven decision-making process [1][2][3][4][5][6][7] has been playing an essential part of the critical responses to the stringent business environment [4,[8][9][10][11][12][13]. Identifying profitable or costly customer is crucial for businesses to maximize the returns, preserve a long-term relationship with the customers, and sustain a competitive advantage [14].…”

Section: Introductionmentioning

confidence: 99%

Business Analytics in Telemarketing: Cost-Sensitive Analysis of Bank Campaigns Using Artificial Neural Networks

et al. 2020

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The banking industry has been seeking novel ways to leverage database marketing efficiency. However, the nature of bank marketing data hindered the researchers in the process of finding a reliable analytical scheme. Various studies have attempted to improve the performance of Artificial Neural Networks in predicting clients’ intentions but did not resolve the issue of imbalanced data. This research aims at improving the performance of predicting the willingness of bank clients to apply for a term deposit in highly imbalanced datasets. It proposes enhanced Artificial Neural Network models (i.e., cost-sensitive) to mitigate the dramatic effects of highly imbalanced data, without distorting the original data samples. The generated models are evaluated, validated, and consequently compared to different machine-learning models. A real-world telemarketing dataset from a Portuguese bank is used in all the experiments. The best prediction model achieved 79% of geometric mean, and misclassification errors were minimized to 0.192, 0.229 of Type I & Type II Errors, respectively. In summary, an interesting Meta-Cost method improved the performance of the prediction model without imposing significant processing overhead or altering original data samples.

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Section: Introductionmentioning

confidence: 99%

Business Analytics in Telemarketing: Cost-Sensitive Analysis of Bank Campaigns Using Artificial Neural Networks

et al. 2020

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“…Compared with the superiority of traditional credit information, big data credit can be measured from two dimensions of efficiency and cost [7]. Because of the three characteristics of timeliness, accuracy, and economies of scale in big data credits, the study believes that big data credits are superior to traditional credit information in terms of efficiency [8]. This paper will focus on the field of personal credit reporting from the cost dimension and then demonstrate the necessity of developing large data credit.…”

Section: Analysis Of Ideamentioning

confidence: 99%

Research on Application of Big Data in Internet Financial Credit Investigation Based on Improved GA-BP Neural Network

Fei-peng

2018

Complexity

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The arrival of the era of big data has provided a new direction of development for internet financial credit collection. First of all, the article introduced the situation of internet finance and traditional credit industry. Based on that, the mathematical model was used to demonstrate the necessity of developing big data financial credit information. Then, the Internet financial credit data are preprocessed, the variables suitable for modeling are selected, and the dynamic credit tracking model of BP neural network based on adaptive genetic algorithm is constructed. It is found that both LM training algorithm and Bayesian algorithm can converge the error to 10e-6 quickly in the model training, and the overall training effect is ideal. Finally, the rule extraction algorithm is used to simulate the test samples. The accuracy rate of each sample method is over 90%, and some accuracy rate is even more than 90%, which indicates that the model is applicable to the credit data of big data in internet finance.

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“…In random forest [18][19] a randomly selected set of attributes is used to split each node. Every node is split using the best split among a subset of predictors that are deliberately chosen randomly at the node.…”

Section: Random Forestmentioning

confidence: 99%

Turnover Prediction of Shares Using Data Mining Techniques : A Case Study

Shashaank¹,

Sruthi²,

Vijayalakshimi³

et al. 2015

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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Business Analytics using Random Forest Trees for Credit Risk Prediction: A Comparison Study

Abstract: In the era of stringent and dynamic business environment, it is crucial for organizations to foresee their clients '

Cited by 50 publications

References 16 publications

Business Analytics in Telemarketing: Cost-Sensitive Analysis of Bank Campaigns Using Artificial Neural Networks

Business Analytics in Telemarketing: Cost-Sensitive Analysis of Bank Campaigns Using Artificial Neural Networks

Research on Application of Big Data in Internet Financial Credit Investigation Based on Improved GA-BP Neural Network

Turnover Prediction of Shares Using Data Mining Techniques : A Case Study

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