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Multi-class misclassification cost matrix for credit ratings in peer-to-peer lending

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  • Haomin Wang
  • Gang Kou
  • Yi Peng

Abstract

Online peer-to-peer (P2P) lending is a new form of loans. Different from traditional banks, lenders provide loans to borrowers directly through P2P platforms. Since many P2P loans are unsecured personal loans, credit rating of loans is vital to control default risk and improve profit for lenders and platforms. Standard binary classifiers are inappropriate in P2P lending because there are multiple credit classes and misclassification costs vary largely across classes in P2P lending. Though there are a few works that studied cost-sensitive classifiers in P2P lending, none of them have analyzed this issue from the perspective of multi-class classifications and measured misclassification costs of different credit grades using real losses and opportunity costs. The objective of this paper is to model credit rating in P2P lending as a cost-sensitive multi-class classification problem. We proposed a misclassification cost matrix for P2P credit grading with a set of equations and models to calculate the costs. An experiment using publicly available data from Lending Club was conducted to validate the usefulness of the proposed misclassification cost matrix. The results showed that the cost-sensitive classifiers can significantly reduce the total cost, which is essential for the survival and profitability of P2P platforms.

Suggested Citation

  • Haomin Wang & Gang Kou & Yi Peng, 2021. "Multi-class misclassification cost matrix for credit ratings in peer-to-peer lending," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 72(4), pages 923-934, March.
    • Handle: RePEc:taf:tjorxx:v:72:y:2021:i:4:p:923-934
    DOI: 10.1080/01605682.2019.1705193
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    Citations

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    Cited by:

    1. Hyunwoo Woo & So Young Sohn, 2022. "A credit scoring model based on the Myers–Briggs type indicator in online peer-to-peer lending," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-19, December.
    2. Çağlar Hamarat & Daniel Broby, 2022. "Regulatory constraint and small business lending: do innovative peer-to-peer lenders have an advantage?," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-25, December.
    3. Joseph P. Hughes & Julapa Jagtiani & Choon-Geol Moon, 2022. "Consumer lending efficiency: commercial banks versus a fintech lender," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-39, December.
    4. Serena Gallo, 2021. "Fintech platforms: Lax or careful borrowers’ screening?," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-33, December.
    5. Ons Triki & Fathi Abid, 2022. "Contingent convertible lease modeling and credit risk management," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-29, December.
    6. Asror Nigmonov & Syed Shams, 2021. "COVID-19 pandemic risk and probability of loan default: evidence from marketplace lending market," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-28, December.
    7. Paulo Cesar Schotten & Leydiana Sousa Pereira & Danielle Costa Morais, 2022. "Credit granting sorting model for financial organizations," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-24, December.
    8. Felix Reichenbach & Martin Walther, 2021. "Signals in equity-based crowdfunding and risk of failure," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-30, December.
    9. Godsway Korku Tetteh, 2023. "Local digital lending development and the incidence of deprivation in Kenya," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-26, December.
    10. Alireza Amirteimoori & Biresh K. Sahoo & Saber Mehdizadeh, 2023. "Data envelopment analysis for scale elasticity measurement in the stochastic case: with an application to Indian banking," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-36, December.
    11. Kamilė Taujanskaitė & Eugenijus Milčius, 2022. "Accelerated Growth of Peer-to-Peer Lending and Its Impact on the Consumer Credit Market: Evidence from Lithuania," Economies, MDPI, vol. 10(9), pages 1-17, September.
    12. Pranith Kumar Roy & Krishnendu Shaw, 2021. "A multicriteria credit scoring model for SMEs using hybrid BWM and TOPSIS," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-27, December.
    13. Puneet Pasricha & Dharmaraja Selvamuthu, 2021. "A Markov regenerative process with recurrence time and its application," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-22, December.
    14. Yanhong Guo & Shuai Jiang & Wenjun Zhou & Chunyu Luo & Hui Xiong, 2021. "A predictive indicator using lender composition for loan evaluation in P2P lending," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-24, December.
    15. Salvador Cruz Rambaud & Joaquín López Pascual & Emilio M. Santandreu, 2023. "A socioeconomic approach to the profile of microcredit holders from the Hispanic minority in the USA," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-25, December.

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