Modelling loss given default in peer-to-peer lending using random forests

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F19%3A39914915" target="_blank" >RIV/00216275:25410/19:39914915 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-981-13-8311-3_12" target="_blank" >https://link.springer.com/chapter/10.1007/978-981-13-8311-3_12</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-981-13-8311-3_12" target="_blank" >10.1007/978-981-13-8311-3_12</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Modelling loss given default in peer-to-peer lending using random forests

Popis výsledku v původním jazyce

Modelling credit risk in peer-to-peer (P2P) lending is increasingly important due to the rapid growth of P2P platforms’ user bases. To support decision making on granting P2P loans, diverse machine learning methods have been used in P2P credit risk models. However, such models have been limited to loan default prediction, without considering the financial impact of the loans. Loss given default (LGD) is used in modelling consumer credit risk to address this issue. Earlier approaches to modelling LGD in P2P lending tended to use multivariate linear regression methods in order to identify the determinants of P2P loans’ credit risk. Here, we show that these methods are not effective enough to process complex features present in P2P lending data. We propose a novel decision support system to LGD modeling in P2P lending. To reduce the problem of overfitting, the system uses random forest (RF) learning in two stages. First, extremely risky loans with LGD = 1 are identified using classification RF. Second, the LGD of the remaining P2P loans is predicted using regression RF. Thus, the non-normal distribution of the LGD values can be effectively modelled. We demonstrate that the proposed system is effective for the benchmark of P2P Lending Club platform as other methods currently used in LGD modelling are outperformed.

Název v anglickém jazyce

Modelling loss given default in peer-to-peer lending using random forests

Popis výsledku anglicky

Modelling credit risk in peer-to-peer (P2P) lending is increasingly important due to the rapid growth of P2P platforms’ user bases. To support decision making on granting P2P loans, diverse machine learning methods have been used in P2P credit risk models. However, such models have been limited to loan default prediction, without considering the financial impact of the loans. Loss given default (LGD) is used in modelling consumer credit risk to address this issue. Earlier approaches to modelling LGD in P2P lending tended to use multivariate linear regression methods in order to identify the determinants of P2P loans’ credit risk. Here, we show that these methods are not effective enough to process complex features present in P2P lending data. We propose a novel decision support system to LGD modeling in P2P lending. To reduce the problem of overfitting, the system uses random forest (RF) learning in two stages. First, extremely risky loans with LGD = 1 are identified using classification RF. Second, the LGD of the remaining P2P loans is predicted using regression RF. Thus, the non-normal distribution of the LGD values can be effectively modelled. We demonstrate that the proposed system is effective for the benchmark of P2P Lending Club platform as other methods currently used in LGD modelling are outperformed.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

50206 - Finance

Projekt

<a href="/cs/project/GA19-15498S" target="_blank" >GA19-15498S: Modelování emocí ve verbální a neverbální manažerské komunikaci pro predikci podnikových finančních rizik</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Intelligent Decision Technologies 2019 : Proceedings of the 11th KES International Conference on Intelligent Decision Technologies (KES-IDT 2019), Vol. 1

ISBN

978-981-13-8310-6

ISSN

2190-3018

e-ISSN

—

Počet stran výsledku

9

Strana od-do

133-141

Název nakladatele

Springer Nature

Místo vydání

Heidelberg

Místo konání akce

St. Julians

Datum konání akce

17. 6. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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