Resampling ensemble model based on data distribution for imbalanced credit risk evaluation in P2P lending

Niu, Kun; Zhang, Zaimei; Liu, Yan; Li, Renfa

doi:10.1016/j.ins.2020.05.040

Cited by 91 publications

(41 citation statements)

References 35 publications

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Credit risk classification has always been a hot issue in scientific research, especially in the context of globalization (Ma and Wang 2020), where it has become increasingly important in the field of financial risk management. Credit risk assessment, the early stage of credit risk management, has attracted much attention from academics and practitioners (Niu et al 2020). How to distinguish bad customers from good, minimize credit risk, and prevent credit fraud in advance are the most important issues for commercial banks and other related credit granting institutions (Yu et al 2008;Wang et al 2020).With the development of information technology, the Internet and numerous intelligent devices produce more and more data, and accordingly, these data reflect an

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confidence: 99%

A high-dimensionality-trait-driven learning paradigm for high dimensional credit classification

2021

Financ Innov

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To solve the high-dimensionality issue and improve its accuracy in credit risk assessment, a high-dimensionality-trait-driven learning paradigm is proposed for feature extraction and classifier selection. The proposed paradigm consists of three main stages: categorization of high dimensional data, high-dimensionality-trait-driven feature extraction, and high-dimensionality-trait-driven classifier selection. In the first stage, according to the definition of high-dimensionality and the relationship between sample size and feature dimensions, the high-dimensionality traits of credit dataset are further categorized into two types: 100 < feature dimensions < sample size, and feature dimensions ≥ sample size. In the second stage, some typical feature extraction methods are tested regarding the two categories of high dimensionality. In the final stage, four types of classifiers are performed to evaluate credit risk considering different high-dimensionality traits. For the purpose of illustration and verification, credit classification experiments are performed on two publicly available credit risk datasets, and the results show that the proposed high-dimensionality-trait-driven learning paradigm for feature extraction and classifier selection is effective in handling high-dimensional credit classification issues and improving credit classification accuracy relative to the benchmark models listed in this study.

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confidence: 99%

A high-dimensionality-trait-driven learning paradigm for high dimensional credit classification

2021

Financ Innov

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“…The experimental results are analyzed and discussed in this section. Considering that the credit data is unbalanced and the overall accuracy is not appropriate to evaluate the models, we use area under roc curve (AUC) [7] as a performance evaluation indicator. All experiments run 5 times, each time using a different random number seed.…”

Section: Results Discussionmentioning

confidence: 99%

“…With the great development of optimization theory and computer technology, machine learning method has gradually become the mainstream of personal credit assessment research, and the performance has been greatly improved. Typical methods include neural network [15]- [17], genetic algorithm [18], support vector machine [19], refusal inference algorithm [6], gradient boosting decision tree [2], [4], et al There are also some works to improve the performance through ensemble models [7], [20]- [22]. These methods solve problems such as increased data size and unbalanced data structure from different angles, and greatly promoted the development of personal credit assessment.…”

Section: Related Workmentioning

confidence: 99%

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A Deep Learning Based Online Credit Scoring Model for P2P Lending

2020

Self Cite

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Credit scoring models have been widely used in traditional financial institutions for many years. Using these models in P2P Lending have limitations. First, the credit data of P2P usually contains dense numerical features and sparse categorical features. Second, the existing credit scoring models are generally cannot be updated online. The loan transaction of P2P lending is very frequent and the new data leads data distribution to change. A credit scoring model without considering data update causes a serious deviation or even failure in subsequent credit assessment. In this paper, we propose a new online integrated credit scoring model (OICSM) for P2P Lending. OICSM integrates gradient boosting decision tree and neural network to make the credit scoring model can handle two types of features more effectively and update online. Offline and online experiments based on real and representative credit datasets are conducted to verify the effectiveness and superiority of the proposed model. Experimental results demonstrate that OICSM can significantly improve performance due to its advantage in deep learning over two features, and it can further correct model deterioration due to its online dynamic update capability.

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“…Ensemble learning has been used in many different applications such as classification of birdsong [10], credit risk evaluation [11], to improve deep learning performance [12], a telemedicine tool framework for lung sounds classification [13].…”

Section: Mehmet Safa Bi̇ngöl Mechatronic Engineering Department Facultmentioning

confidence: 99%

Classification of Mushroom Data Set by Ensemble Methods

Yıldırım¹

2024

RIiM

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Due to disease of mushrooms, it is very important to classify mushrooms for predicting the best quality mushrooms. There are many methods to analyse the main parts of mushrooms. For above mentioned descriptions; in this simulation study five types of classification algorithm are employed to predict the structure of mushrooms. Mushroom dataset is used to predict the classes of mushrooms. The results are improved that these methods will be used to predict exact mushrooms features and classifications in real time approaches.

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Resampling ensemble model based on data distribution for imbalanced credit risk evaluation in P2P lending

Cited by 91 publications

References 35 publications

A high-dimensionality-trait-driven learning paradigm for high dimensional credit classification

A high-dimensionality-trait-driven learning paradigm for high dimensional credit classification

A Deep Learning Based Online Credit Scoring Model for P2P Lending

Classification of Mushroom Data Set by Ensemble Methods

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