A Novel behavioral scoring model for estimating probability of default over time in peer-to-peer lending

Zhao, Wang; Jiang, Cuiqing; Ding, Yong; Lyu, Xiaozhong; Liu, Yao

doi:10.1016/j.elerap.2017.12.006

Cited by 69 publications

(28 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The previously obtained optimum model is put through repeated k-folded cross-validation (three repetitions and ten folds) to fine-tune to secure a reliably accurate figure, independently of the specific training and testing subsets. That is a robust method for estimating classification accuracy and reducing the amount of bias in the estimate [31]- [35].…”

Section: B Research Methodologymentioning

confidence: 99%

Success Factors in Peer-to-Business (P2B) Crowdlending: A Predictive Approach

2019

View full text Add to dashboard Cite

Peer-to-Business (P2B) crowdlending is gaining importance among companies seeking funding. However, not all projects get the same take-up by the crowd. Thus, this study aims to determine the key factors that drive non-professional investors to choose a given loan in an online environment. To this purpose, we have analyzed 243 crowdlending campaigns on October.eu platform. We have obtained a series of variables from the analyzed loans using logistic regression. Results indicate that loan amount, loan term and overall credit rating are the key predictors of non-professional lender P2B crowdlending success. These findings may be useful for predicting whether the crowd will subscribe to a loan request or not. This information would help businesses to modify specific loan characteristics (if possible) to make their loans more attractive or could even lead companies to consider a different financial option. It could also help platforms select and adapt project parameters to secure their success.

show abstract

Section: B Research Methodologymentioning

confidence: 99%

Success Factors in Peer-to-Business (P2B) Crowdlending: A Predictive Approach

2019

View full text Add to dashboard Cite

show abstract

“…The latter compares the performance of an artificial neural network approach with that of alternatives such as logistic regression, linear discriminant analysis, decision trees and support vector machines. Machine learning methods can be found together with survival models in the articles by Wang et al [31] and Jiang et al [32].…”

Section: Machine Learning and Credit Risk Modeling In P2p Lendingmentioning

confidence: 99%

Explainability of a Machine Learning Granting Scoring Model in Peer-to-Peer Lending

Garzón

Arroyo

Caparrini³

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Peer-to-peer (P2P) lending demands effective and explainable credit risk models. Typical machine learning algorithms offer high prediction performance, but most of them lack explanatory power. However, this deficiency can be solved with the help of the explainability tools proposed in the last few years, such as the SHAP values. In this work, we assess the well-known logistic regression model and several machine learning algorithms for granting scoring in P2P lending. The comparison reveals that the machine learning alternative is superior in terms of not only classification performance but also explainability. More precisely, the SHAP values reveal that machine learning algorithms can reflect dispersion, nonlinearity and structural breaks in the relationships between each feature and the target variable. Our results demonstrate that is possible to have machine learning credit scoring models be both accurate and transparent. Such models provide the trust that the industry, regulators and end-users demand in P2P lending and may lead to a wider adoption of machine learning in this and other risk assessment applications where explainability is required.

show abstract

“…The proposed SurvXGBoost model is compared with three non-parametric survival models, namely the Cox PH model, RSF, and survival GBDT model (Chen et al, 2013). The Cox PH model is a state-of-the-art benchmark that has been extensively considered in existing literature (Bellotti & Crook, 2009;Wang et al, 2018). We have also employed a time-varying Cox PH model following Bellotti and Crook (2013) to capture the time-varying effects of some features.…”

Section: Model Validationmentioning

confidence: 99%

“…Another solution concerns on the fusion of survival models with machine learning. By far, survival models have been integrated with ANN (Baesens et al, 2005) and RF (Wang et al, 2018). In a benchmark study, Dirick et al (2017) compared several classical survival models used in credit scoring and revealed that Cox PH-based models provided a good performance especially in combination with spline methods.…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Credit Scoring Model Based on Survival Gradient Boosting Decision Tree Approach

Xia

et al. 2020

Technological and Economic Development of Economy

View full text Add to dashboard Cite

Credit scoring, which is typically transformed into a classification problem, is a powerful tool to manage credit risk since it forecasts the probability of default (PD) of a loan application. However, there is a growing trend of integrating survival analysis into credit scoring to provide a dynamic prediction on PD over time and a clear explanation on censoring. A novel dynamic credit scoring model (i.e., SurvXGBoost) is proposed based on survival gradient boosting decision tree (GBDT) approach. Our proposal, which combines survival analysis and GBDT approach, is expected to enhance predictability relative to statistical survival models. The proposed method is compared with several common benchmark models on a real-world consumer loan dataset. The results of out-of-sample and out-of-time validation indicate that SurvXGBoost outperform the benchmarks in terms of predictability and misclassification cost. The incorporation of macroeconomic variables can further enhance performance of survival models. The proposed SurvXGBoost meanwhile maintains some interpretability since it provides information on feature importance.

show abstract

A Novel behavioral scoring model for estimating probability of default over time in peer-to-peer lending

Cited by 69 publications

References 33 publications

Success Factors in Peer-to-Business (P2B) Crowdlending: A Predictive Approach

Success Factors in Peer-to-Business (P2B) Crowdlending: A Predictive Approach

Explainability of a Machine Learning Granting Scoring Model in Peer-to-Peer Lending

A Dynamic Credit Scoring Model Based on Survival Gradient Boosting Decision Tree Approach

Contact Info

Product

Resources

About