How can lenders prosper? Comparing machine learning approaches to identify profitable peer-to-peer loan investments

DOI10.1016/j.ejor.2021.01.047zbMath1487.91146OpenAlexW3126370939MaRDI QIDQ2240005

Christophe Mues, Trevor Fitzpatrick

Publication date: 5 November 2021

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ejor.2021.01.047

zbMATH Keywords

investment analysispredictive modellingcredit scoringensemble learningP2P lending

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Credit risk (91G40)

Related Items (3)

Credit default prediction from user-generated text in peer-to-peer lending using deep learning ⋮ The profitability of online loans: a competing risks analysis on default and prepayment ⋮ Operational research and artificial intelligence methods in banking

Uses Software

• pls
• LIBLINEAR
• Kernlab
• earth
• glmnet
• mlr
• ranger
• word2vec
• h2o
• XGBoost
• GitHub
• robustlmm
• LiblineaR
• BERT
• hmeasure

Cites Work

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• Development and application of consumer credit scoring models using profit-based classification measures
• Benchmarking state-of-the-art classification algorithms for credit scoring: an update of research
• Instance-based credit risk assessment for investment decisions in P2P lending
• Multivariate adaptive regression splines
• Learning to Rank for Information Retrieval
• Regularization and Variable Selection Via the Elastic Net
• Ridge Regression: Biased Estimation for Nonorthogonal Problems
• Random forests

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