Probabilistic Regression and Missing Values Compensation for P2P Lending

DOI

10.2991/edmi-19.2019.96How to use a DOI?

Keywords

Random Forest, Gaussian Process, Data Compensation, P2P lending.

Abstract

With the development of the Internet, peer-to-peer lending has become widely recognized and accepted by the public. P2P lending platforms are overgrowing around the world, which provides many conveniences to medium borrowers who have difficulty in traditional financial institutions. However, due to the qualification of P2P lending platforms and the lack of supervision, many of these platforms have no ability to disclose information to the authorities, which will increase industry risk and instability in the financial system. So, our work focuses on the missing data of P2P lending platforms from the perspective of predicting losing values. We highlight our work on dealing with missing values using Random Forest instead of traditional methods. After that, we applied the Gaussian Process Regression to explore the relationship between variables and got a conclusion that the loan balance of a particular platform can be predicted.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Huixi He
AU  - Kazumitsu Nawata
PY  - 2019/08
DA  - 2019/08
TI  - Probabilistic Regression and Missing Values Compensation for P2P Lending
BT  - Proceedings of the 1st International Symposium on Economic Development and Management Innovation (EDMI 2019)
PB  - Atlantis Press
SP  - 570
EP  - 575
SN  - 2352-5428
UR  - https://doi.org/10.2991/edmi-19.2019.96
DO  - 10.2991/edmi-19.2019.96
ID  - He2019/08
ER  -