Combination of Random Forests and Neural Networks in Social Lending

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Journal of Financial Risk Management

ISSN Print: 2167-9533
ISSN Online: 2167-9541
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"Combination of Random Forests and Neural Networks in Social Lending"
written by Yijie Fu,
published by Journal of Financial Risk Management, Vol.6 No.4, 2017

has been cited by the following article(s):

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CrossRef

[1]	Peer-to-peer loan default prediction using machine learning methods


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[3]	Predictive analysis of default risk in peer-to-peer lending platforms: empirical evidence from LendingClub
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[4]	ENHANCING QUALITY AND REDUCING COSTS IN THE PRINTING INDUSTRY
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[5]	DeRisk: An Effective Deep Learning Framework for Credit Risk Prediction over Real-World Financial Data
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[6]	Modelování kreditního rizika na P2P trzích
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[7]	Credit Risk Modelling on P2P Markets
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[8]	Ensemble of diverse deep neural networks with pseudo-labels for repayment prediction in social lending
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[9]	Machine Learning-Based Fault Detection and Diagnosis of Internal Combustion Engines Using an Optical Crank Angle Encoder
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[10]	Default Prediction for Loan Lenders Using Machine Learning Algorithms
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[11]	EXCERPT
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[12]	Kalp hastalık risk tahmini için Python aracılığıyla sınıflandırıcı algoritmalarının performans değerlendirmesi
	Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen …, 2021

[13]	A Credit Risk Predicting Hybrid Model Based on Deep Learning Technology
	2021

[14]	Loan default prediction of Chinese P2P market: a machine learning methodology
	Scientific Reports, 2021

[15]	Deep learning for repayment prediction in leasing companies
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[16]	Improving Investment Suggestions for Peer-to-Peer Lending via Integrating Credit Scoring into Profit Scoring
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[17]	A DBM-Based Ensemble Method for Improving Default Risk Prediction of Peer-to-Peer (P2P) Lending
	International Journal of e-Education, e-Business, e-Management and e-Learning, 2020

[18]	Spectral Clustering and Cost-Sensitive Deep Neural Network-Based Undersampling Approach for P2P Lending Data
	2020

[19]	Data-driven Investment Decisions in P2P Lending: Strategies of Integrating Credit Scoring and Profit Scoring
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[20]	An empirical evaluation of writing style features in cross-topic and cross-genre documents in authorship identification
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[21]	An ensemble semi-supervised learning method for predicting defaults in social lending
	2019

[22]	Credit Risk Analysis in Peer to Peer Lending Data set: Lending Club
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[23]	Predicting repayment of borrows in peer‐to‐peer social lending with deep dense convolutional network
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[24]	Análise automática da presença cognitiva em discussões online escritas em português
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[25]	Deep Dense Convolutional Networks for Repayment Prediction in Peer-to-Peer Lending
	International Joint Conference SOCO’18-CISIS’18-ICEUTE’18, 2018

[1]	Credit scoring using multi-task Siamese neural network for improving prediction performance and stability Expert Systems with Applications, 2025 DOI:10.1016/j.eswa.2024.125327

[2]	Determining the creditworthiness of borrowers in Blockchain-based peer-to-peer lending systems using machine learning 2024 IEEE 9th International Conference on Adaptive Science and Technology (ICAST), 2024 DOI:10.1109/ICAST61769.2024.10856478

[3]	Estimation of default and pricing for invoice trading (P2B) on crowdlending platforms Financial Innovation, 2024 DOI:10.1186/s40854-024-00632-4

[4]	Ensemble of diverse deep neural networks with pseudo-labels for repayment prediction in social lending Science Progress, 2022 DOI:10.1177/00368504221124004

[5]	Ensemble of diverse deep neural networks with pseudo-labels for repayment prediction in social lending Science Progress, 2022 DOI:10.1177/00368504221124004

[6]	Loan default prediction of Chinese P2P market: a machine learning methodology Scientific Reports, 2021 DOI:10.1038/s41598-021-98361-6

[7]	Why segmentation matters: a Machine Learning approach for predicting loan defaults in the Peer-to-Peer (P2P) Financial Ecosystem Risk Management Magazine, 2021 DOI:10.47473/2020rmm0089

[8]	Kalp hastalık risk tahmini için Python aracılığıyla sınıflandırıcı algoritmalarının performans değerlendirmesi Deu Muhendislik Fakultesi Fen ve Muhendislik, 2021 DOI:10.21205/deufmd.2021236926

[9]	Loan default prediction of Chinese P2P market: a machine learning methodology Scientific Reports, 2021 DOI:10.1038/s41598-021-98361-6

[10]	Improving Investment Suggestions for Peer-to-Peer Lending via Integrating Credit Scoring into Profit Scoring Proceedings of the 2020 ACM Southeast Conference, 2020 DOI:10.1145/3374135.3385272

[11]	Improving Investment Suggestions for Peer-to-Peer Lending via Integrating Credit Scoring into Profit Scoring Proceedings of the 2020 ACM Southeast Conference, 2020 DOI:10.1145/3374135.3385272

[12]	Improving Investment Suggestions for Peer-to-Peer Lending via Integrating Credit Scoring into Profit Scoring Proceedings of the 2020 ACM Southeast Conference, 2020 DOI:10.1145/3374135.3385272

[13]	International Joint Conference SOCO’18-CISIS’18-ICEUTE’18 Advances in Intelligent Systems and Computing, 2019 DOI:10.1007/978-3-319-94120-2_13

[14]	Predicting repayment of borrows in peer‐to‐peer social lending with deep dense convolutional network Expert Systems, 2019 DOI:10.1111/exsy.12403

[15]	International Joint Conference SOCO’18-CISIS’18-ICEUTE’18 Advances in Intelligent Systems and Computing, 2019 DOI:10.1007/978-3-319-94120-2_13

[16]	An ensemble semi-supervised learning method for predicting defaults in social lending Engineering Applications of Artificial Intelligence, 2019 DOI:10.1016/j.engappai.2019.02.014

[17]	Predicting repayment of borrows in peer‐to‐peer social lending with deep dense convolutional network Expert Systems, 2019 DOI:10.1111/exsy.12403

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