“Time-to-profit scorecards for revolving credit”

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

doi:10.1016/j.ejor.2015.09.052

Cited by 6 publications

(5 citation statements)

References 24 publications

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“…Recent studies develop P2P credit scoring [5,6,7], although none propose profit scoring. A profit scoring DSS allows for selection of the most profitable borrowers, which is related to customer lifetime value [8]. The calculation of customer profitability for a store selling products on credit requires data from the management accounting system, such as the margin of each product sold to each customer.…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%

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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“…Profit-scoring modeling has emerged as part of a credit granting decision-making tool. Borrowers are grouped according to profitability ratios instead of default probabilities and credit losses (Sanchez-Barrios et al, 2016).…”

Section: Methodsmentioning

confidence: 99%

“…Decision trees (Srinivisan & Kim, 1987), artificial neural networks (Malhotra & Malhotra, 2003), k-nearest neighbors (West, 2000) and support vector machines (Schebesch & Stecking, 2005) are examples of techniques used for credit-scoring modeling. Logistic regression (So, Thomas, & Seow, 2014), artificial neural networks (Verbraken, Bravo, & Baesens, 2014), survival analysis (Sanchez-Barrios et al, 2016) and chi-square automatic interaction detector (Serrano-Cinca & Gutiérrez-Nieto, 2016) stand out among the techniques used for profit scoring modeling.…”

Section: Methodsmentioning

confidence: 99%

“…This approach is based on the concept that nondefaulting borrowers may not generate sufficient revenue to offset the costs of maintaining their accounts, whereas defaulting borrowers may be profitable if they actively engage in credit transactions and honor most of their commitments (Sanchez- Barrios, Andreeva, & Ansell, 2016). The methodology addresses decisions regarding the selection of the desired risk combined with a return level and the formulation of strategies to acquire and retain profitable clients (Sanchez-Barrios et al, 2016). Lima et al (2008) argue that the agency conflict in credit unions can be mitigated by the adoption of efficient internal controls and clear governance rules.…”

Section: Introductionmentioning

confidence: 99%

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Estimating credit and profit scoring of a Brazilian credit union with logistic regression and machine-learning techniques

Paula

Artes

Ayres

et al. 2019

RAUSP

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Purpose Although credit unions are nonprofit organizations, their objectives depend on the efficient management of their resources and credit risk aligned with the principles of the cooperative doctrine. This paper aims to propose the combined use of credit scoring and profit scoring to increase the effectiveness of the loan-granting process in credit unions. Design/methodology/approach This sample is composed by the data of personal loans transactions of a Brazilian credit union. Findings The analysis reveals that the use of statistical methods improves significantly the predictability of default when compared to the use of subjective techniques and the superiority of the random forests model in estimating credit scoring and profit scoring when compared to logit and ordinary least squares method (OLS) regression. The study also illustrates how both analyses can be used jointly for more effective decision-making. Originality/value Replacing subjective analysis with objective credit analysis using deterministic models will benefit Brazilian credit unions. The credit decision will be based on the input variables and on clear criteria, turning the decision-making process impartial. The joint use of credit scoring and profit scoring allows granting credit for the clients with the highest potential to pay debt obligation and, at the same time, to certify that the transaction profitability meets the goals of the organization: to be sustainable and to provide loans and investment opportunities at attractive rates to members.

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“…Various profit scoring approaches have been proposed in the literature of consumer credit risk. These approaches have been based on analytical techniques including Markov chain modeling (Thomas et al, 2002a;Thomas et al, 2002b), survival analysis (Narain 1992;Banasik et al, 1999;Sanchez-Barrios et al 2016), expected profit maximization (Finlay 2008;Finlay 2020, Stewart 2011Verbraken 2014), and regression (Buckley and James 1979;Lai and Ying, 1994). Markov chain techniques have been used to build stochastic models of complex situations and consumer behaviors.…”

Section: Literature Reviewmentioning

confidence: 99%

Wide and deep learning for peer-to-peer lending

Bastani¹,

Asgari

Namavari

2019

Expert Systems with Applications

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This paper proposes a two-stage scoring approach to help lenders decide their fund allocations in the peer-to-peer (P2P) lending market. The existing scoring approaches focus on only either probability of default (PD) prediction, known as credit scoring, or profitability prediction, known as profit scoring, to identify the best loans for investment. Credit scoring fails to deliver the main need of lenders on how much profit they may obtain through their investment. On the other hand, profit scoring can satisfy that need by predicting the investment profitability. However, profit scoring completely ignores the class imbalance problem where most of the past loans are non-default. Consequently, ignorance of the class imbalance problem significantly affects the accuracy of profitability prediction. Our proposed two-stage scoring approach is an integration of credit scoring and profit scoring to address the above challenges. More specifically, stage 1 is designed as credit scoring to identify non-default loans while the imbalanced nature of loan status is considered in PD prediction. The loans identified as non-default are then moved to stage 2 for prediction of profitability, measured by internal rate of return. Wide and deep learning is used to build the predictive models in both stages to achieve both memorization and generalization. Extensive numerical studies are conducted based on real-world data to verify the effectiveness of the proposed approach. The numerical studies indicate our two-stage scoring approach outperforms the existing credit scoring and profit scoring approaches.

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“Time-to-profit scorecards for revolving credit”

Cited by 6 publications

References 24 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

Estimating credit and profit scoring of a Brazilian credit union with logistic regression and machine-learning techniques

Wide and deep learning for peer-to-peer lending

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