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

Serrano‐Cinca, Carlos; Nieto, Begoña Gutiérrez

doi:10.1016/j.dss.2016.06.014

Cited by 201 publications

(136 citation statements)

References 34 publications

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“…Although numerous profit scoring approaches have been reported in the literature of consumer credit risk, a very few of these works have been focused on the context of P2P lending. One of those instances is Serrano-Cinca and Gutiérrez-Nieto (2016) wherein the authors proposed IRR as a measure of profitability of loans, and studied determinant factors of IRR. These factors were very similar to the determinant factors of PD studied by Serrano-Cinca et al (2015) (more details on these variables are provided in Section 4).…”

Section: Literature Reviewmentioning

confidence: 99%

“…The work of Ref. Serrano-Cinca and Gutiérrez-Nieto (2016) completely ignores PD associated to the loans, and, instead, directly predicts IRR. Hence, their approach does not consider the imbalanced nature of the loans.…”

Section: Literature Reviewmentioning

confidence: 99%

“…If the borrower defaults on the loan, depending on the amount of loan delinquency, the IRR could theoretically become a value ranging from negative infinity to a small negative value. The proposed profit scoring approach in (Serrano-Cinca and Gutiérrez-Nieto, 2016) can be used to score the loans based on their predicted IRRs. Subsequently, the lenders might select the loans with the highest IRR.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Wide and deep learning for peer-to-peer lending

Bastani¹,

Asgari

Namavari

2019

Expert Systems with Applications

View full text Add to dashboard Cite

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“…The Basel Committee for banking supervision requested that all banks and financial institutions should implement powerful credit scoring systems to aid in estimating credit-risk levels and different risk exposures and to differentiate among customers in offering precision credit services. Financial institutions must apply risk assessment techniques and credit scoring systems in their procedures (Cinca & Gutiérrez-Nieto, 2016). In general, credit scoring consists of a group of decision models and the underlying techniques that give support to lenders when extending credit to customers.…”

Section: Introductionmentioning

confidence: 99%

Generalized fuzzy soft sets theory‐based novel hybrid ensemble credit scoring model

Zhang

Feng

2018

Int. J Fin Econ

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In banking and peer‐to‐peer loan applicant firms, customer credit scores have numerous applications in risk control and precision marketing. Numerous credit scoring techniques act as classification methods. In this paper, the main issue is simultaneous and hybrid utilization of the feature selection (FS) algorithm and ensemble learning classification algorithms with respect to their parameter settings to achieve higher performance in the proposed credit scoring model. As a result, this paper reports a hybrid data mining model of generalized fuzzy soft sets (GFSS) theory‐based ensemble learning classification algorithms based on three stages. The first stage addresses data gathering and preprocessing. The second stage uses the adaptive elastic net‐based FS algorithm to eliminate irrelevant or weakly correlated variables. After the appropriate variables are chosen, they are applied to the proposed ensemble model. In the third stage, GFSS theory is exploited to construct a novel weight assignment mechanism for each individual credit scoring model in the ensemble model according to performance. Several comparisons are conducted to validate the proposed model using real world credit datasets. The experimental results, analysis, and statistical tests prove the ability of the proposed approach to improve classification performance against all of the base classifier, hybrid, and combination methods in terms of average accuracy, area under the curve, H‐measure, and Brier score.

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“…e first one is the empirical research of investor's behavior in the online loan platform to clarify the impact factors of investor's risk preference [1] and investor's choice [2][3][4]. e second aspect is borrowers' credit scoring and their probability of default [5][6][7][8], which is important to control the risk for P2P lending. For the borrowers, however, there are few efforts to investigate their borrowing benefits and costs or discuss the important principles of utility enhancing.…”

Section: Introductionmentioning

confidence: 99%

Optimizing the Borrowing Limit and Interest Rate in P2P System: From Borrowers’ Perspective

Tang

2018

Scientific Programming

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P2P (peer-to-peer) lending is an emerging online service that allows individuals to borrow money from unrelated person without the intervention of traditional financial intermediaries. In these platforms, borrowing limit and interest rate are two of the most notable elements for borrowers, which directly influence their borrowing benefits and costs, respectively. To that end, this paper introduces a BP neural network interval estimation (BPIE) algorithm to predict the borrowers' borrowing limit and interest rate based on their characteristics and simultaneously develops a new parameter optimization algorithm (GBPO) based on the genetic algorithm and our BP neural network predictive model to optimize them. Using real-world data from http://ppdai.com, the experimental results show that our proposed model achieves a good performance. is research provides a new perspective from borrowers in exploring the P2P lending. e case base and proposed knowledge are the two contributions for FinTech research.

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

Cited by 201 publications

References 34 publications

Wide and deep learning for peer-to-peer lending

Wide and deep learning for peer-to-peer lending

Generalized fuzzy soft sets theory‐based novel hybrid ensemble credit scoring model

Optimizing the Borrowing Limit and Interest Rate in P2P System: From Borrowers’ Perspective

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