Monetary and relative scorecards to assess profits in consumer revolving credit

Barrios, Luis Javier Sanchez; Andreeva, Galina; Ansell, Jake

doi:10.1057/jors.2013.66

Cited by 14 publications

(8 citation statements)

References 21 publications

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“…The results suggest a profit-based scoring system segmented by risk and predicting spend improves upon a risk-only strategy Barrios et al (2013) [14] Absolute and relative scorecards for assessing profits in consumer revolving credit. Data originate from a Colombian lending institution…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…Other studies develop profit scoring for credit cards and consumer credit [12,13,14,15,16,3]; however, the lack of data resulted in the use of customer profit proxies. To the best of our knowledge, there are no previous studies using the IRR as a dependent variable.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…This measure is a proxy for customer value and is also used by Andreeva et al [29]. Barrios et al [14] utilized the cumulative profit relative to the outstanding debt for scoring purposes. They recognize the limitations of using this proxy because a standard accounting return requires a more detailed allocation of the total assets used by each customer.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

See 2 more Smart Citations

The use of profit scoring as an alternative to credit scoring systems in peer-to-peer (P2P) lending

Serrano‐Cinca

Nieto

2016

Decision Support Systems

206

124

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Section: Accepted Manuscriptmentioning

confidence: 99%

Section: Accepted Manuscriptmentioning

confidence: 99%

Section: Accepted Manuscriptmentioning

confidence: 99%

See 1 more Smart Citation

The use of profit scoring as an alternative to credit scoring systems in peer-to-peer (P2P) lending

Serrano‐Cinca

Nieto

2016

Decision Support Systems

206

124

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“…Generally, the main linear statistical tool used for developing credit scoring model is the logistic regression. In the context of P2P lending, logistic regression has been used in the studies of Andreeva et al (2007); Barrios et al (2014); Emekter et al (2015); Serrano-Cinca and Gutiérrez-Nieto (2016). This approach aims to classify the dependent variable in two groups, characterized by a different loan status [1=default; 0=no default] in which borrowers are classified by logistic regression, specified by the following model:…”

Section: Scoring Modelsmentioning

confidence: 99%

Latent factor models for credit scoring in P2P systems

Ahelegbey

Giudici

Misheva

2019

Physica A: Statistical Mechanics and its Applications

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Peer-to-Peer (P2P) FinTech platforms allow cost reduction and service improvement in credit lending. However, these improvements may come at the price of a worse credit risk measurement, and this can hamper lenders and endanger the stability of a financial system. We approach the problem of credit risk for Peer-to-Peer (P2P) systems by presenting a latent factor-based classification technique to divide the population into major network communities in order to estimate a more efficient logistic model. Given a number of attributes that capture firm performances in a financial system, we adopt a latent position model which allow us to distinguish between communities of connected and not-connected firms based on the spatial position of the latent factors. We show through empirical illustration that incorporating the latent factor-based classification of firms is particularly suitable as it improves the predictive performance of P2P scoring models.

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“…Thirdly, our empirical application contributes to modeling credit risk in SMEs particularly engaged in P2P lending. For related works on P2P lending via logistic regression, see Andreeva et al (2007); Barrios et al (2014); Emekter et al (2015); Serrano-Cinca and Gutiérrez-Nieto (2016). We model the credit score of over 15000 SMEs engaged in P2P credit services across Southern Europe.…”

Section: Introductionmentioning

confidence: 99%

Factorial Network Models To Improve P2P Credit Risk Management

2019

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This paper investigates how to improve statistical-based credit scoring of SMEs involved in P2P lending. The methodology discussed in the paper is a factor network-based segmentation for credit score modeling. The approach first constructs a network of SMEs where links emerge from comovement of latent factors, which allows us to segment the heterogeneous population into clusters. We then build a credit score model for each cluster via lasso logistic regression. We compare our approach with the conventional logistic model by analyzing the credit score of over 15000 SMEs engaged in P2P lending services across Europe. The result reveals that credit risk modeling using our network-based segmentation achieves higher predictive performance than the conventional model.

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Monetary and relative scorecards to assess profits in consumer revolving credit

Cited by 14 publications

References 21 publications

The use of profit scoring as an alternative to credit scoring systems in peer-to-peer (P2P) lending

The use of profit scoring as an alternative to credit scoring systems in peer-to-peer (P2P) lending

Latent factor models for credit scoring in P2P systems

Factorial Network Models To Improve P2P Credit Risk Management

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