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Blockchain-based Smart P2P Lending using Neural Networks

by Bhaumik Choksi, Alisha Sawant, Sai Subhasree Pakina
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 180 - Number 35
Year of Publication: 2018
Authors: Bhaumik Choksi, Alisha Sawant, Sai Subhasree Pakina
10.5120/ijca2018916888

Bhaumik Choksi, Alisha Sawant, Sai Subhasree Pakina . Blockchain-based Smart P2P Lending using Neural Networks. International Journal of Computer Applications. 180, 35 ( Apr 2018), 51-55. DOI=10.5120/ijca2018916888

@article{ 10.5120/ijca2018916888,
author = { Bhaumik Choksi, Alisha Sawant, Sai Subhasree Pakina },
title = { Blockchain-based Smart P2P Lending using Neural Networks },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2018 },
volume = { 180 },
number = { 35 },
month = { Apr },
year = { 2018 },
issn = { 0975-8887 },
pages = { 51-55 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume180/number35/29292-2018916888/ },
doi = { 10.5120/ijca2018916888 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:02:45.380218+05:30
%A Bhaumik Choksi
%A Alisha Sawant
%A Sai Subhasree Pakina
%T Blockchain-based Smart P2P Lending using Neural Networks
%J International Journal of Computer Applications
%@ 0975-8887
%V 180
%N 35
%P 51-55
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Over the past decade, there has been an exponential growth in the number and scale of online lending and crowdfunding platforms. However, these platforms lack a reliable and transparent metric to predict the credit-worthiness of an applicant. They also have a single point of failure and are vulnerable to certain security issues. This paper proposes a Blockchain-based decentralized lending platform that uses deep learning to predict the risk associated with an applicant. The paper also discusses how such a system can be implemented and deployed. The experimental results show how ensemble training can help lower the bias of individual neural networks and provide better predictions for this use case.

References
  1. Byanjankar, Ajay, Markku Heikkilä, and Jozsef Mezei. "Predicting credit risk in peer-to-peer lending: A neural network approach." Computational Intelligence, 2015 IEEE Symposium Series on. IEEE, 2015.
  2. Alaraj, Maher. Evaluating Consumer Loans Using Neural Networks Ensembles. ICMLEME, 2014.
  3. Ghatge, A.R. Ensemble Neural Network Strategy for Predicting Credit Default Evaluation. IJEIT, 2013.
  4. Amira Kamil , Ajith Abraham Modeling Consumer Loan Default Prediction Using Ensemble Neural Networks. ICCEEE,2013
  5. Jin, Yu, and Yudan Zhu. "A data-driven approach to predict default risk of loan for online Peer-to-Peer (P2P) lending." Communication Systems and Network Technologies (CSNT), 2015 Fifth International Conference on. IEEE, 2015.
  6. De Filippi, Primavera. "Blockchain-based Crowdfunding: what impact on artistic production and art consumption?." (2015).
  7. Jacynycz, Viktor, et al. "Betfunding: A distributed bounty-based crowdfunding platform over ethereum." Distributed Computing and Artificial Intelligence, 13th International Conference. Springer, Cham, 2016.
Index Terms

Computer Science
Information Sciences

Keywords

Deep Learning P2P Lending Credit-risk Blockchain Decentralized Neural Networks