Detecting the Abnormal Lenders from P2P Lending Data

Author:

Li Haifeng,Zhang Yuejin,Zhang Ning,Jia Hengyue

Publisher

Elsevier BV

Subject

General Engineering

Reference6 articles.

1. Stability in consumer credit scores: Level and direction of FICO score drift as a precursor to mortgage default and prepayment;Smith;Journal of Housing Economics,2011

2. An empirical comparison of conventional techniques, neural networks and the three stage hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) model for credit scoring analysis: The case of Turkish credit card data;Soner;European Journal of Operational Research,2012

3. Anatomy of the credit score;Arya;Journal of Economic Behavior & Ornanization,2013

4. A trust model for online peer-to-peer lending: a lender's perspective;Chen;Information Technology Management,2014

5. Evaluating credit risk and loan performance in online Peer-to-Peer(P2P) lending;Emerkter;Applied Economics,2014

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1. Feature selection in P2P lending based on hybrid genetic algorithm with machine learning;International Journal of Computers and Applications;2023-10-31

2. Optimizing Completion Rate of P2P Lending Services Analysis based on Big Data Analysis;Proceedings of the 2023 4th International Conference on Computer Science and Management Technology;2023-10-13

3. Artificial intelligence in Peer-to-peer lending in India: a cross-case analysis;International Journal of Emerging Markets;2022-02-01

4. TrustLend: Using Borrower Trustworthiness for Lending on Ethereum;Proceedings of the 19th International Conference on Security and Cryptography;2022

5. Risk-return modelling in the p2p lending market: Trends, gaps, recommendations and future directions;Electronic Commerce Research and Applications;2021-09

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