Credit risk modeling on data with two timestamps in peer-to-peer lending by gradient boosting

Zhou, Ligang; Fujita, Hamido; Ding, Hao; Ma, Rui

doi:10.1016/j.asoc.2021.107672

Cited by 22 publications

(11 citation statements)

References 25 publications

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“…e Decision-Making Problem with Cost Constraints. Obviously, the cost is one of the most important constraints in the decision-making process, which cannot be ignored [41]. Although every alternative for dealing with emergencies is costly, while there are still wide gaps among different alternatives.…”

Section: 4mentioning

confidence: 99%

A Closed-Loop Method for Multiperiod Intelligent Information Processing with Cost Constraints under the Fuzzy Environment

Wang

Cao

et al. 2022

Computational Intelligence and Neuroscience

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From trivial matters in life to major scientific projects related to the fate of mankind, decision-making is everywhere. Whether high-quality decisions can be made often directly affects the development of affairs, especially when sudden disasters occur. As the basis of decision-making, data are crucial. The continuously probabilistic linguistic set, a data structure of the fuzzy mathematics, is selected in the paper to collect original data after careful comparisons, because this data structure can fully consider the hesitation of decision-makers and the fuzziness of complex problems. Although all alternatives are costly, the costs of different alternatives still vary greatly; obviously, the low-cost alternative is better than others when the same predetermined goal can be achieved, which is one of the research objectives and characteristics of this paper. Different from other researchers who only take the cost as one of the decision-making indicators, the algorithm proposed in the paper pays much more attention on the cost reduction. When dealing with an emergency, it is often difficult to solve the problem by taking measures only once; usually, multiple rounds of measures are needed. Each round of decision-making has both connections and differences, and the multiround decision-making model is proposed and built in the paper. Different from traditional linear structures, the model mainly adopts the closed-loop structure, which divides the whole process into multiple sub-decision-making points, the severities measured at the current time point will be compared with the values estimated at the latter time point, and then, the differences will be input into the system, the corresponding automatic adjustment modules will be activated immediately according to the values. The accuracy of the system can be verified and adjusted in time by the closed-loop control module. Finally, several experiments are carried out and the results show that the algorithm proposed in the paper is more effective and the cost is lower.

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Section: 4mentioning

confidence: 99%

A Closed-Loop Method for Multiperiod Intelligent Information Processing with Cost Constraints under the Fuzzy Environment

Wang

Cao

et al. 2022

Computational Intelligence and Neuroscience

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“…The works of many Russian and foreign researchers are devoted to the mechanisms of analysis of the bank's credit risk: Eremin, Moskvicheva, and Melik-Aslanova (2020); Gorelov, Davydov, Silaev, and Tikhonov (2018); Ryapukhin, Kabakov, and Zaripov (2019); Tarasova, Nikolenko, Gorbunov, and Semina (2018); Zaripov, Murakaev, and Ryapukhin (2020); Shabaltina, Egorova, Agaphonov, and Ermolina (2020); Nguyen, Marmier, and Gourc (2013); Chao and Franck (2012); Zaynullina (2021); Zelentsova and Tikhonov (2020); Gyazova and Gorelov (2021); Oskarsdóttir and Bravo (2021); Zhou, Fujita, Ding, and Ma (2021). In general, in scientific literature, risk is understood as the probability of deviations of actual performance from those expected under conditions of uncertainty.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In this study, a multi-layer network model was developed to assess credit risk and the relationships between borrowers, and a quantitative assessment was carried out of the degree of risk of default of the borrower (Oskarsdóttir & Bravo, 2021). In the framework of forecasting borrowers' credit risk, the Bayesian approach is the most appropriate in various scenarios (Zhou et al, 2021). As a tool for making decisions to achieve the maximum level of fulfillment of obligations, a risk-oriented model is used to determine the existence of a connection between the enterprise project management system and the risk management system.…”

Section: Literature Reviewmentioning

confidence: 99%

Buffer Model Determination of Systemic Strategic Risks for Borrowers in Force Majeure Circumstances

Burdina

Bondarenko

Moskvicheva

2022

5002

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This study substantiates the concept of systemic strategic risks for borrowers and buffers systemic strategic risk in force majeure to the analyze the credit risk of banks. The authors studied the existing approaches used to assess the creditworthiness of borrowers and the credit risk of banks. The factors that influence the level of credit risk of borrowers are also identified. Quantitative and qualitative parameters of a borrower's creditworthiness, used in banking risk management, are also determined. An economic and mathematical model is proposed to determine the systemic strategic risk buffer for borrowers in force majeure circumstances. It is proposed that technology should be used to create a systemic strategic risk buffer for borrowers for hedging in force majeure circumstances as a monetary policy tool and include Basel III in the requirements of the banking agreement.

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“…Categorical Boosting (CatBoost) and Light Gradient Boosting Machine (LightGBM) are two boosting algorithms proposed by Dorogush et al (2018) and Ke et al (2017), respectively. They have shown outstanding classification performance in the loan default prediction (Daoud, 2019; Xia et al, 2019, 2020; Zhou et al, 2021). Hence, besides the abovementioned RF, XGBoost, and AdaBoost, we also explore the feature selection performance of CatBoost and LightGBM.…”

Section: Introductionmentioning

confidence: 99%

A comparative study of combining tree‐based feature selection methods and classifiers in personal loan default prediction

Guo

Zhou

2022

Journal of Forecasting

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Personal credit data usually contain a large number of features, some of which do not significantly contribute to the performance of default prediction models. Screening features through appropriate methods is essential to improve the efficiency of prediction models. However, little attention has been paid to feature selection methods in the area of personal loan default prediction. In this study, we employ random forest (RF), XGBoost, Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), and Light Gradient Boosting Machine (LightGBM) as base algorithms of wrapper and embedded methods to select features and use these algorithms as classifiers to predict personal loan default. We find that when classical filter methods are used to select features, the number of selected features needs to be large enough to enable tree‐based classifiers to get their best performance. However, when the tree‐based algorithm is used to select features, it only needs to select a small number of features to deliver a satisfactory classification performance. AdaBoost, Chi2, and F‐score are found to be ideal feature selection methods in the area of personal credit default prediction. Moreover, we find that it is better to use different algorithms in feature selection and classification; AdaBoost and CatBoost perform the best among all classifiers.

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Credit risk modeling on data with two timestamps in peer-to-peer lending by gradient boosting

Cited by 22 publications

References 25 publications

A Closed-Loop Method for Multiperiod Intelligent Information Processing with Cost Constraints under the Fuzzy Environment

A Closed-Loop Method for Multiperiod Intelligent Information Processing with Cost Constraints under the Fuzzy Environment

Buffer Model Determination of Systemic Strategic Risks for Borrowers in Force Majeure Circumstances

A comparative study of combining tree‐based feature selection methods and classifiers in personal loan default prediction

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