Credit risk prediction in an imbalanced social lending environment

Namvar, Anahita; Siami, Mohammad; Rabhi, Fethi A.; Naderpour, Mohsen

doi:10.2991/ijcis.11.1.70

Cited by 57 publications

(50 citation statements)

References 27 publications

(58 reference statements)

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“…The result shows that the hybrid approach does not perform well compared to oversampling and undersampling methods. The undersampling approach shows the best result, especially in random undersampling techniques with the Random Forest as the classifier [7].…”

Section: Previous Workmentioning

confidence: 99%

“…One of the prominent techniques to solve the problem is using the resampling. Resampling techniques are an approach to generates an adjust training dataset before building the classification model [7].…”

Section: Samplingmentioning

confidence: 99%

“…Adaboost with the conjunction of twelve different classifiers and using majority votings. Zareapoor et al [4] Application of Credit Card Fraud Detection: Based on Bagging Ensemble Classifier K-Nearest Neighbor, Support Vector Machine, Bagging based on Decision Tree and Naïve Bayes Awoyemi et al [5] Credit card fraud detection using Machine Learning Techniques: A Comparative Analysis Hybrid sampling method, K-Nearest Neighbor, Naïve Bayes, and Logistic Regression Namvar et al [7] Credit risk prediction in an imbalanced social lending environment…”

Section: Introductionmentioning

confidence: 99%

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Effectiveness Undersampling Method and Feature Reduction in Credit Card Fraud Detection

Trisanto¹,

Rismawati²,

Mulya³

et al. 2020

IJIES

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Credit card fraud is an issue that has affected Indonesia payment system over a decade. Sometimes, the result of the fraud used for terrorism and other crimes. Financial loss is not the only problem that is affected caused by credit card fraud but also Indonesia images in international trade, e-commerce, and the merchant. Currently, a trusted and secured banking payment system is crucial for both customers and banks. The problem from credit card fraud dataset is the data have many features and imbalanced class, this problem leads the paper to propose undersampling technique and feature reduction methods. In this paper we proposed two stage-feature reduction technique because a stage feature reduction could not find the optimal features. On the other hands, we are also applied Instance Hardness Threshold sampling and Random undersampling to deal with imbalance data. The two-stage feature reduction is chosen to eliminate the ineffective feature that cannot eliminate using only one feature reduction. The model from the implemented machine learning methods is evaluated using accuracy, specificity, recall, and Matthews Correlation Coefficient. We implemented our proposed approaches in the ULB credit card fraud detection dataset. According to the result, the undersampling gives a boost in performances which improve the recall and MCC score, the IHT undersampling provide goods results, and in some cases, the result can predict all the test set correctly. However, the two-stages feature reduction fails to improve the accuracy, precision, recall, and MCC score. In one case, the method reduced the accuracy score to 0.302.

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Section: Previous Workmentioning

confidence: 99%

Section: Samplingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Effectiveness Undersampling Method and Feature Reduction in Credit Card Fraud Detection

Trisanto¹,

Rismawati²,

Mulya³

et al. 2020

IJIES

View full text Add to dashboard Cite

show abstract

“…Byanjankar et al [2] consider neural networks as means to classifying loan applicants into default and non-default groups. Namvar et al [13] developed a credit risk prediction framework that compares different resampling approaches in combination with outstanding classifiers. They demonstrate that random undersampling and random forest classifiers are an efficient combination of classifier and resampling strategy for credit risk prediction in P2P lending.…”

Section: A Loan Evaluation In P2p Lendingmentioning

confidence: 99%

Handling Uncertainty in Social Lending Credit Risk Prediction with a Choquet Fuzzy Integral Model

Namvar

Naderpour

2018

2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

Self Cite

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As one of the main business models in the financial technology field, peer-to-peer (P2P) lending has disrupted traditional financial services by providing an online platform for lending money that has remarkably reduced financial costs. However, the inherent uncertainty in P2P loans can result in huge financial losses for P2P platforms. Therefore, accurate risk prediction is critical to the success of P2P lending platforms. Indeed, even a small improvement in credit risk prediction would be of benefit to P2P lending platforms. This paper proposes an innovative credit risk prediction framework that fuses base classifiers based on a Choquet fuzzy integral. Choquet integral fusion improves creditworthiness evaluations by synthesizing the prediction results of multiple classifiers and finding the largest consistency between outcomes among conflicting and consistent results. The proposed model was validated through experimental analysis on a realworld dataset from a well-known P2P lending marketplace. The empirical results indicate that the combination of multiple classifiers based on fuzzy Choquet integrals outperforms the best base classifiers used in credit risk prediction to date. In addition, the proposed methodology is superior to some conventional combination techniques. Keywords-Choquet fuzzy integral; fuzzy measure; credit risk prediction; peer-to-peer lendingI.

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“…Related studies generally select features such as the borrower characteristics and credit history from the lending data or extract new features for predicting the repayment (Malekipirbazari & Aksakalli, ). They attempted to model repayment prediction and credit assessment by employing a machine learning algorithm (Namvar, Siami, Rabhi, & Naderpour, ).…”

Section: Introductionmentioning

confidence: 99%

Predicting repayment of borrows in peer‐to‐peer social lending with deep dense convolutional network

Kim

Cho

2019

Expert Systems

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In peer‐to‐peer lending, it is important to predict the repayment of the borrower to reduce the lender's financial loss. However, it is difficult to design a powerful feature extractor for predicting the repayment as user and transaction data continue to increase. Convolutional neural networks automatically extract useful features from big data, but they use only high‐level features; hence, it is difficult to capture a variety of representations. In this study, we propose a deep dense convolutional network for repayment prediction in social lending, which maintains the borrower's semantic information and obtains a good representation by automatically extracting important low‐ and high‐level features simultaneously. We predict the repayment of the borrower by learning discriminative features depending on the loan status. Experimental results on the Lending Club dataset show that our model is more effective than other methods. A fivefold cross‐validation is performed to run the experiments.

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Credit risk prediction in an imbalanced social lending environment

Cited by 57 publications

References 27 publications

Effectiveness Undersampling Method and Feature Reduction in Credit Card Fraud Detection

Effectiveness Undersampling Method and Feature Reduction in Credit Card Fraud Detection

Handling Uncertainty in Social Lending Credit Risk Prediction with a Choquet Fuzzy Integral Model

Predicting repayment of borrows in peer‐to‐peer social lending with deep dense convolutional network

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