Next Article in Journal
Asymmetric Wealth Effect between US Stock Markets and US Housing Market and European Stock Markets: Evidences from TAR and MTAR
Previous Article in Journal
Cryptocurrency Trading and Downside Risk
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:

Credit Scoring for Peer-to-Peer Lending

Daniel Felix Ahelegbey
* and
Paolo Giudici
Department of Economics and Management Sciences, University of Pavia, 27100 Pavia, Italy
Author to whom correspondence should be addressed.
Risks 2023, 11(7), 123;
Submission received: 12 June 2023 / Revised: 1 July 2023 / Accepted: 4 July 2023 / Published: 7 July 2023


This paper shows how to improve the measurement of credit scoring by means of factor clustering. The improved measurement applies, in particular, to small and medium enterprises (SMEs) involved in P2P lending. The approach explores the concept of familiarity which relies on the notion that the more familiar/similar things are, the closer they are in terms of functionality or hidden characteristics (latent factors that drive the observed data). The approach uses singular value decomposition to extract the factors underlying the observed financial performance ratios of SMEs. We then cluster the factors using the standard k-mean algorithm. This enables us to segment the heterogeneous population into clusters with more homogeneous characteristics. The result shows that clusters with relatively fewer number of SMEs produce a more parsimonious and interpretable credit scoring model with better default predictive performance.

1. Introduction

When it comes to the measurement of credit scoring, the traditional concept of one model fits all may work well only for firms or individuals with capacity, credit access, cash, and/or collateral. Such models usually do not work for people with no financial history or collateral even if they have payback capabilities. Continuing with the traditional credit scoring system will not help a significant proportion of SMEs. It is therefore vital and crucial to develop alternative credit scoring models tailored in a way to enable SMEs without traditional financial history but with payback capabilities based on alternative means to have a credit score that enables them to gain access to credit.
Recent advancements gradually transforming the traditional economic and financial system is the emergence of digital-based systems. Such systems present a paradigm shift from traditional intra-organizational systems to customer-oriented technological (digital) systems. Financial technological (“FinTech”) companies are gradually gaining ground in major developed economies across the world. The emergence of business-to-customer (B2C), customer-to-customer (C2C), provider-oriented business-to-business (B2B), and peer-to-peer (P2P) platforms are typical examples of FinTech systems. Thus, Fintech offers solutions that differ from traditional institutions regarding the providers and the interaction types as well as regarding the banking and insurance processes they support (Haddad and Hornuf 2019; Puschmann 2017). These platforms aim at facilitating credit services by connecting individual lenders with individual borrowers without the interference of traditional banks as intermediaries. Such platforms serve as a digital financial market and have significantly improved the customer experience in terms of cost saving and speed of the services to both individual borrowers and lenders, as well as small business owners.
Despite the various advantages of current fintech systems, the existing digital platform systems inherit some of the challenges of traditional credit risk management. Credit scoring is purely based on the data available on a borrower that signals their financial worth and ability to pay back loans. In addition, they are characterized by the asymmetry of information and by a strong interconnectedness among their users (see, e.g., Chen et al. 2022; Giudici and Polinesi 2021; Giudici et al. 2020, 2022) that makes distinguishing healthy and risky credit applicants difficult, thus affecting credit issuers. There is, therefore, a need to explore methods that can help improve the credit scoring of individuals or companies that engage in P2P credit services (see El Annas et al. 2023; Jiang et al. 2018; Lyócsa et al. 2022; Ma et al. 2021; Xia et al. 2020).
This paper investigates how a factor clustering-based approach to segment a population in order to improve the statistical-based credit score for small and medium enterprises (SMEs) involved in P2P lending. The methodology employed in this paper extends the similarity of latent factors recently adopted by Ahelegbey et al. (2019a, 2019b). The approach explores the concept of familiarity as a signal for functional relationships among financial institutions. The familiarity concept relies on the notion that the more familiar things are, the closer they are in terms of functionality or hidden characteristics (latent factors that drive the observed data). By this reasoning, we postulate that the more familiar the latent factors of SME performance ratios, the closer they are in terms of either holding securities with similar features or pursuing similar financial strategies or models. Such features make SMEs become more identical and increase the probability of exposure to common risk factors.
We contribute to the literature on the application of factor models in finance (see, e.g., Dungey and Gajurel 2015; Dungey et al. 2005; Forbes and Rigobon 2002; Fox and Dunson 2015; Lopes and Carvalho 2007; Nakajima and West 2013). In this paper, we cluster the factors that drive the observed financial data, enabling us to segment the population and estimate a logistic model based on the sample segmentation.
Our empirical application contributes to the literature on credit scoring in P2P lending systems (see Ahelegbey et al. 2019a, 2019b; Andreeva et al. 2007; Barrios et al. 2014; Emekter et al. 2015; Giudici et al. 2020; Serrano-Cinca and Gutiérrez-Nieto 2016, for related works). We provide an application of our approach to a well-known dataset consisting of over 15,000 SMEs involved in P2P credit services across Europe. Our results show that segmentation of the heterogenous market based on latent characteristics presents a more efficient scheme to credit risk measurement that achieves higher performance than the conventional approach.
The paper is organized as follows. Section 2 presents the econometric methodology, Section 3 discusses the application to a credit scoring database provided by a European rating agency for P2P platforms, and Section 4 presents concluding remarks.

2. Econometric Methodology

2.1. Segmented Logistic Model

Let Y i be a binary variable that represents the observed loan default status of firm-i, where Y i = 1 if the firm has defaulted on its loan, and Y i = 0 if it has not defaulted. Denote with X i a p-dimensional vector of observed financial features of firm-i that predicts its creditworthiness. The conventional approach to credit scoring is to model the probability of the default given the observed firm financial features via a one-model-fits-all logistic regression. In this application, we assume the firms can be classified into groups according to some similarities in the latent characteristics of their observed features. Suppose there exist k non-overlapping groups of firms. We model the probability of the default of firms-i in group l as a logistic model via the log-odds function given by
log π i ( l ) 1 π i ( l ) = β 0 ( l ) + j = 1 p X i j ( l ) β j ( l )
where π i ( l ) = P ( Y i ( l ) = 1 | X i ( l ) ) is the probability of default of firm-i in group l, β 0 ( l ) is a constant term of the group, β j ( l ) , j = 1 , , p , is a logistic regression coefficient that shows the change in the logit of the probability associated with a unit change in the j-th predictor holding all other predictors constant.

2.2. Factor Model with SVD

Let X be the observed data matrix of n institutions, each with p number of features measuring financial performance ratios. We denote with X i , the i-th institution which corresponds to the i-th row of X. We proceed under the assumption that the observed data matrix X can be approximated via singular value decomposition (SVD) given by (Hoff 2007)
X = U D V
where U is of dimensions n × r , V is of dimensions p × r , r < p , with the columns of U and V denoting the left and right singular vectors of X, respectively, and D is an r-dimensional diagonal matrix of the square roots of the non-zero eigenvalues of X X and X X .

2.3. Clustering Latent Coordinates

To classify the n firms into k non-overlapping groups, we consider a clustering scheme such that “similar” firms belong to the same group and “different” firms go into different groups. In this application, we use the latent coordinates of the firms in U as points in a plane (or some higher-dimensional space).
Given that U is a matrix of coordinates of n points in an r-dimensional space, the i-th row represents the coordinates of the i-th firm, while the j-th column of U represents the coordinates of the institutions on the j-th axis. For simplicity, we plot the first three dimensions of U as the default dimension of the latent positions. This correlates with most applications involving multi-dimensional scaling and provides a convenient framework to visualize the position of agents/firms in a 3-D space.
Typical clustering methods discussed in the literature range from centroid-based methods (k-means) to density-based, distribution-based, and hierarchical methods. In this application, we follow the centroid-based method of k-means clustering due to its simplicity, popularity, and successful application in market segmentation (see James et al. 2013). The choice of the number of clusters can be considered as a model selection problem since each cluster corresponds to a different statistical model (see Bai and Ng 2002; D’Angelo et al. 2023; Fraley and Raftery 2002; Handcock et al. 2007; Hoff et al. 2002).

3. Empirical Application

3.1. Data

We evaluate the effectiveness of our proposed model by considering a well-known dataset for P2P studies (Ahelegbey et al. 2019a, 2019b; Giudici et al. 2020). The dataset consists of 15,045 SMEs engaged in P2P lending on digital platforms across Southern Europe, with each institution containing 24 financial features in ratios constructed from official financial records in 2015. The data were obtained from the European External Credit Assessment Institution (ECAI).
Table 1 presents a description and summary of the financial ratios based on default status. In all, the data consist of 1632 (10.85%) defaulted institutions and 13,413 (89.15%) non-defaulted companies. Due to differences in the scale of the values of the sampled financial variables, we standardize each series to a zero mean and unit variance.
Figure 1 presents a 3-D scatterplot of the SMEs’ latent positions based on singular value decomposition of the observed features. The coordinates of defaulted SMEs are in red circles and the non-default SMEs are in green triangles.
Table 2 shows the statistics of the number and percentage of the defaulted status of SMEs based on k-means clustering of the latent coordinates. From the 3-D plot in Figure 1 two clusters are evident. Thus, we choose k = 2 . The table shows that 14,866 (98.81%) of the SMEs are classified in Cluster 1, while the rest 179 (1.19%) are in Cluster 2. Of those in Cluster 1, 10.80% have defaulted and 89.20% have not. Of those in Cluster 2, 14.53% are defaulted SMEs, while 85.47% are not.

3.2. Credit Score Modeling

Table 3 reports the estimated coefficients of the logistic regression for the full sample and the clustered samples. We remark that the results of the table are derived via a thorough activity of model selection, aimed at obtaining the best fit statistical model using stepwise logistic regression. The estimation of the models is carried out on the training sample which we set to be 70% of the sample. Given that 98.81% of the full sample is classified into Cluster 1, it is therefore not surprising that the credit score for the Full Sample and Cluster 1 have the same key drivers. However, we observe that for Cluster 2, the determinants of the credit score are somewhat different. There are however, some common drivers for Cluster 1 and 2, such as V3 (Total Assets/Total Liabilities), V4 (Current Assets/Current Liabilities), V14 (EBITDA/Operating Revenues), and V21 (Trade Receivables/Operating Revenues). Despite these common terms, the result shows that the majority of the key drivers of credit score for those in Cluster 1 are not significant determinants for those in Cluster 2.

3.3. Performance of Default Predicting Accuracy

We evaluate the default prediction accuracy of the estimated models on the testing sample and compare the performance in terms of the standard area under the curve (AUC) derived from the receiver operator characteristic (ROC) curve. The AUC depicts the true positive rate (TPR) against the false positive rate (FPR) depending on some threshold. TPR is the number of correct positive predictions divided by the total number of positives. FPR is the ratio of false positive predictions to overall negatives.
Table 4 shows the results of the area under the ROC curve of the full sample and clustered sample models. The result shows that the Full Sample model and Cluster 1 achieved an 82.34% prediction rate, while Cluster 2 reported a rate of 96.77%. However, the combined performance of the clustered sample model attains 82.62%. Thus, the clustered sample shows a slightly higher gain in predictive performance compared to the full sample approach.
Table 4 also reports the DeLong test of the pairwise comparison of the AUC of the full sample and that of the clustered sample models. The DeLong test is a statistical test for determining whether the AUCs of two models are significantly different (see DeLong et al. 1988). We conduct a one-sided DeLong test under the following hypothesis:
H 0 : AUC ( Full sample ) AUC ( Clustered sample ) H 1 : AUC ( Full sample ) < AUC ( Clustered sample )
The results of the test as shown by the table indicates that the difference between the ROC of the clustered sample and the full sample leads to a DeLong test statistics equal to 1.688 , with a corresponding p-value equal to 0.046 , which indicates that the null hypotheses can be rejected and that the clustered sample achieves a performance significantly superior with respect to the full sample.

3.4. Implication of the Study

Market segmentation is the subdivision of markets into discrete groups that share similar characteristics. Such segmentation can be a useful scheme for credit risk measurement especially in systems like P2P, that are characterized by agents with heterogeneous characteristics. Market segmentation is simply a classification problem that involves assigning individuals in a population into discrete non-overlapping groups.
Our proposal could be useful to credit risk managers and investors involved in P2P lending. Because credit risk in P2P systems is not born by the platform but, rather by the investors, the ability of the latter to distinguish healthy and risky clients plays a crucial role in credit risk mitigation. Our study provides evidence that by clustering SMEs into two non-overlapping groups based on latent characteristics, investors, as well as risk managers, can identify and distinguish between common and dissimilar financial variables that drives the probability of default of a loan issued to a client.

4. Conclusions

This paper contributes to the strand of empirical studies to improve credit scoring for SMEs engaged in peer-to-peer platforms. We present a factor clustering-based approach to segment a heterogeneous population into groups with more homogeneous characteristics. The approach uses singular value decomposition to extract the factors underlying the observed financial performance ratios of SMEs. These factors are then classified into clusters via a k-mean algorithm. We then model the credit score for each sub-population via logistic regression.
The empirical application of our approach is demonstrated by applying our proposed methodology to evaluate the probability of default of 15,045 SMEs engaged in P2P lending across Europe. Our factor clustering approach to credit score modeling is shown to produce an efficient framework to analyze the latent positions of SMEs engaged in a P2P platform and provides a way to segment a heterogeneous market/customers into clusters with more homogeneous characteristics. The result shows that clusters with relatively fewer SMEs produce a more parsimonious and interpretable credit scoring model with better default predictive performance.
A limitation of our study could be related to our choice of clustering method and the number of clusters since such decisions strongly impact the results obtained. Thus, a more rigorous and robust clustering method can be advanced to improve the results of our study. Another possible limitation of this study is the application of latent-based clustering of SMEs instead of segmentation based on observed financial information. Future research can be advanced in these directions to improve the contribution of our study.

Author Contributions

Conceptualization, D.F.A. and P.G.; methodology, D.F.A. and P.G.; software, D.F.A.; validation, D.F.A.; formal analysis, D.F.A. and P.G.; investigation, D.F.A. and P.G.; resources, P.G.; data curation, D.F.A.; writing—original draft preparation, D.F.A.; writing—review and editing, P.G.; visualization, D.F.A.; supervision, P.G.; project administration, P.G.; funding acquisition, P.G. All authors have read and agreed to the published version of the manuscript.


This research was funded by Italian MUR PON, contract number n. 22-G-14923-1.

Data Availability Statement

Data is avalilable upon request to the authors.

Conflicts of Interest

The authors declare no conflict of interest.


  1. Ahelegbey, Daniel Felix, Paolo Giudici, and Branka Hadji-Misheva. 2019a. Factorial Network Models To Improve P2P Credit Risk Management. Frontiers in Artificial Intelligence 2: 8. [Google Scholar] [CrossRef] [Green Version]
  2. Ahelegbey, Daniel Felix, Paolo Giudici, and Branka Hadji-Misheva. 2019b. Latent Factor Models For Credit Scoring in P2P Systems. Physica A: Statistical Mechanics and Its Applications 522: 112–21. [Google Scholar] [CrossRef] [Green Version]
  3. Andreeva, Galina, Jake Ansell, and Jonathan Crook. 2007. Modelling Profitability Using Survival Combination Scores. European Journal of Operational Research 183: 1537–49. [Google Scholar] [CrossRef]
  4. Bai, Jushan, and Serena Ng. 2002. Determining the Number of Factors in Approximate Factor Models. Econometrica 70: 191–221. [Google Scholar] [CrossRef] [Green Version]
  5. Barrios, Luis Javier Sánchez, Galina Andreeva, and Jake Ansell. 2014. Monetary and Relative Scorecards to Assess Profits in Consumer Revolving Credit. Journal of the Operational Research Society 65: 443–53. [Google Scholar] [CrossRef] [Green Version]
  6. Chen, Xiao, Zhaohui Chong, Paolo Giudici, and Bihong Huang. 2022. Network Centrality Effects in Peer to Peer Lending. Physica A: Statistical Mechanics and Its Applications 600: 127546. [Google Scholar] [CrossRef]
  7. D’Angelo, Silvia, Marco Alfò, and Michael Fop. 2023. Model-based Clustering for Multidimensional Social Networks. Journal of the Royal Statistical Society Series A: Statistics in Society 186: 481–507. [Google Scholar] [CrossRef]
  8. DeLong, Elizabeth R., David M. DeLong, and Daniel L. Clarke-Pearson. 1988. Comparing the Areas under Two or More Correlated Receiver Operating Characteristic Curves: A Nonparametric Approach. Biometrics 44: 837–45. [Google Scholar] [CrossRef]
  9. Dungey, Mardi, and Dinesh Gajurel. 2015. Contagion and Banking Crisis–International Evidence for 2007–2009. Journal of Banking and Finance 60: 271–83. [Google Scholar] [CrossRef] [Green Version]
  10. Dungey, Mardi, Renée Fry, Brenda González-Hermosillo, and Vance L. Martin. 2005. Empirical Modelling of Contagion: A Review of Methodologies. Quantitative Finance 5: 9–24. [Google Scholar] [CrossRef] [Green Version]
  11. El Annas, Monir, Badreddine Benyacoub, and Mohamed Ouzineb. 2023. Semi-supervised Adapted HMMs for P2P Credit Scoring Systems with Reject Inference. Computational Statistics 38: 149–69. [Google Scholar] [CrossRef]
  12. Emekter, Riza, Yanbin Tu, Benjamas Jirasakuldech, and Min Lu. 2015. Evaluating Credit Risk and Loan Performance in Online Peer-to-Peer (P2P) Lending. Applied Economics 47: 54–70. [Google Scholar] [CrossRef]
  13. Forbes, Kristin J., and Roberto Rigobon. 2002. No Contagion, Only Interdependence: Measuring Stock Market Comovements. The Journal of Finance 57: 2223–61. [Google Scholar] [CrossRef]
  14. Fox, Emily B., and David B. Dunson. 2015. Bayesian Nonparametric Covariance Regression. The Journal of Machine Learning Research 16: 2501–42. [Google Scholar]
  15. Fraley, Chris, and Adrian E. Raftery. 2002. Model-based Clustering, Discriminant Analysis, and Density Estimation. Journal of the American Statistical Association 97: 611–31. [Google Scholar] [CrossRef]
  16. Giudici, Paolo, and Gloria Polinesi. 2021. Crypto price discovery through correlation networks. Annals of Operations Research 1: 443–57. [Google Scholar] [CrossRef]
  17. Giudici, Paolo, Branka Hadji-Misheva, and Alessandro Spelta. 2020. Network based credit risk models. Quality Engineering 2: 199–211. [Google Scholar] [CrossRef]
  18. Giudici, Paolo, Thomas Leach, and Paolo Pagnottoni. 2022. Libra or Librae? Basket based stable coins. Finance Research Letters 44: 102504. [Google Scholar]
  19. Haddad, Christian, and Lars Hornuf. 2019. The Emergence of the Global Fintech Market: Economic and Technological Determinants. Small Business Economics 53: 81–105. [Google Scholar] [CrossRef] [Green Version]
  20. Handcock, Mark S., Adrian E. Raftery, and Jeremy M. Tantrum. 2007. Model-Based Clustering for Social Networks. Journal of the Royal Statistical Society: Series A (Statistics in Society) 170: 301–54. [Google Scholar] [CrossRef] [Green Version]
  21. Hoff, Peter D. 2007. Model Averaging and Dimension Selection for the Singular Value Decomposition. Journal of the American Statistical Association 102: 674–85. [Google Scholar] [CrossRef]
  22. Hoff, Peter D., Adrian E. Raftery, and Mark S. Handcock. 2002. Latent Space Approaches to Social Network Analysis. Journal of the American Statistical Association 97: 1090–98. [Google Scholar] [CrossRef]
  23. James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. 2013. An Introduction to Statistical Learning. Berlin/Heidelberg: Springer, vol. 112. [Google Scholar]
  24. Jiang, Cuiqing, Zhao Wang, Ruiya Wang, and Yong Ding. 2018. Loan Default Prediction by Combining Soft Information Extracted From Descriptive Text in Online Peer-to-Peer Lending. Annals of Operations Research 266: 511–29. [Google Scholar] [CrossRef]
  25. Lopes, Hedibert Freitas, and Carlos Marinho Carvalho. 2007. Factor Stochastic Volatility with Time-Varying Loadings and Markov Switching Regimes. Journal of Statistical Planning and Inference 137: 3082–91. [Google Scholar] [CrossRef]
  26. Lyócsa, Štefan, Petra Vašaničová, Branka Hadji Misheva, and Marko Dávid Vateha. 2022. Default or Profit Scoring Credit Systems? Evidence from European and US Peer-to-Peer Lending Markets. Financial Innovation 8: 32. [Google Scholar] [CrossRef]
  27. Ma, Zhengwei, Wenjia Hou, and Dan Zhang. 2021. A credit Risk Assessment Model of Borrowers in P2P Lending Based on BP Neural Network. PLoS ONE 16: e0255216. [Google Scholar] [CrossRef]
  28. Nakajima, Jouchi, and Mike West. 2013. Bayesian Analysis of Latent Threshold Dynamic Models. Journal of Business and Economic Statistics 31: 151–64. [Google Scholar] [CrossRef] [Green Version]
  29. Puschmann, Thomas. 2017. Fintech. Business & Information Systems Engineering 59: 69–76. [Google Scholar]
  30. Serrano-Cinca, Carlos, and Begoña Gutiérrez-Nieto. 2016. The Use of Profit Scoring as an Alternative to Credit Scoring Systems in Peer-to-Peer Lending. Decision Support Systems 89: 113–22. [Google Scholar] [CrossRef]
  31. Xia, Yufei, Lingyun He, Yinguo Li, Nana Liu, and Yanlin Ding. 2020. Predicting Loan Default in Peer-to-Peer Lending Using Narrative Data. Journal of Forecasting 39: 260–80. [Google Scholar] [CrossRef]
Figure 1. A 3-D scatterplot of the firm’s latent positions based on singular value decomposition of observed features. Coordinates of defaulted SMEs are in red circles and non-default SMEs are in green triangles.
Figure 1. A 3-D scatterplot of the firm’s latent positions based on singular value decomposition of observed features. Coordinates of defaulted SMEs are in red circles and non-default SMEs are in green triangles.
Risks 11 00123 g001
Table 1. Description and summary statistics of the financial ratios based on default status.
Table 1. Description and summary statistics of the financial ratios based on default status.
VarFormula (Description)Active (Mean)Defaulted (Mean)
V1(Total Assets − Shareholders Funds)/Shareholders Funds8.879.08
V2(Longterm debt + Loans)/Shareholders Funds1.251.32
V3Total Assets/Total Liabilities1.511.07
V4Current Assets/Current Liabilities1.61.06
V5(Current Assets − Current assets: stocks)/Current Liabilities1.240.79
V6(Shareholders Funds + Non current liabilities)/Fixed Assets8.075.99
V7EBIT/Interest paid26.39−2.75
V8(Profit (loss) before tax + Interest paid)/Total Assets0.05−0.13
V9P/L after tax/Shareholders Funds0.02−0.73
V10Operating Revenues/Total Assets1.381.27
V11Sales/Total Assets1.341.25
V12Interest Paid/(Profit before taxes + Interest Paid)0.210.08
V13EBITDA/Interest Paid40.915.71
V14EBITDA/Operating Revenues0.08−0.12
V16Constraint EBIT0.130.56
V17Constraint PL before tax0.160.61
V18Constraint Financial PL0.930.98
V19Constraint P/L for period0.190.64
V20Trade Payables/Operating Revenues100.3139.30
V21Trade Receivables/Operating Revenues67.59147.12
V22Inventories/Operating Revenues90.99134.93
V23Total Revenue35572083
V24Industry Classification on NACE code45664624
Total number of institutions (%)13,413 (89.15%)1632 (10.85%)
Table 2. Statistics of the defaulted status of SMEs according to k-mean clustering of the latent coordinates.
Table 2. Statistics of the defaulted status of SMEs according to k-mean clustering of the latent coordinates.
StatusCluster 1Cluster 2
Table 3. Stepwise logistic regression coefficients.
Table 3. Stepwise logistic regression coefficients.
Full SampleCluster (1)Cluster (2)
V2 −0.1981 **
V3−0.5779 ***−0.5409 ***−4.7532 **
V4−0.2761 ***−0.4121 ***−0.5076 *
V5 0.1882
V60.0023 *
V70.0041 ***0.0043 ***
V8−2.2462 ***−2.2547 ***
V9 −1.3393 **
V10−0.3603 **−0.2687 *
V110.4119 ***0.3458 **2.8614
V120.1621 **0.1610 **
V13−0.0023 **−0.0024 **
V14−0.6840 ***−0.7531 ***5.0525 ***
V15 −3.9867 ***
V160.7136 ***0.6889 ***
V180.3775 *0.4068 **
V190.7888 ***0.8021 ***
V20 0.0007 **
V210.0021 ***0.0021 ***0.0019 **
V220.0005 **0.0005 *0.0025
V23−0.00002 ***−0.00003 ***
Constant−2.2426 ***−2.3916 ***3.0007
Log Likelihood−3167.4570−3118.5110−28.6924
Akaike Inf. Crit.6370.91406275.023077.3849
Note: * p < 0.1; ** p < 0.05; *** p < 0.01.
Table 4. Comparing area under the ROC curve of the full sample and clustered sample models.
Table 4. Comparing area under the ROC curve of the full sample and clustered sample models.
Full SampleCluster (1)Cluster (2)
Full SampleCombine Cluster 1 and 2
DeLong test−1.6880.046**
Note: ** p < 0.05.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Ahelegbey, D.F.; Giudici, P. Credit Scoring for Peer-to-Peer Lending. Risks 2023, 11, 123.

AMA Style

Ahelegbey DF, Giudici P. Credit Scoring for Peer-to-Peer Lending. Risks. 2023; 11(7):123.

Chicago/Turabian Style

Ahelegbey, Daniel Felix, and Paolo Giudici. 2023. "Credit Scoring for Peer-to-Peer Lending" Risks 11, no. 7: 123.

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop