Profit-sensitive machine learning classification with explanations in credit risk: The case of small businesses in peer-to-peer lending-Reference-Cited by-同舟云学术

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Profit-sensitive machine learning classification with explanations in credit risk: The case of small businesses in peer-to-peer lending

Published:2024-09 Issue: Volume:67 Page:101428
ISSN:1567-4223
Container-title:Electronic Commerce Research and Applications
language:en
Short-container-title:Electronic Commerce Research and Applications

Author:

Ariza-Garzón Miller-Janny^ORCID,Arroyo Javier^ORCID,Segovia-Vargas María-Jesús^ORCID,Caparrini Antonio^ORCID

Funder

European Cooperation in Science and Technology

Publisher

Elsevier BV

Reference80 articles.

1. Cost-sensitive risk induced Bayesian inference bagging (RIBIB) for credit card fraud detection;Akila;J. Comput. Sci.,2018

2. Example-dependent cost-sensitive credit cards fraud detection using SMOTE and Bayes minimum risk;Almhaithawi;SN Appl. Sci.,2020

3. Explainability of a Machine Learning Granting Scoring Model in Peer-to-Peer Lending;Ariza-Garzon;IEEE Access,2020

4. Risk-return modelling in the p2p lending market: Trends, gaps, recommendations and future directions;Ariza-Garzón;Electron. Commer. Res. Appl.,2021

5. A multi-objective instance-based decision support system for investment recommendation in peer-to-peer lending;Babaei;Expert Syst. Appl.,2020

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