Location
Hilton Waikoloa Village, Hawaii
Event Website
http://hicss.hawaii.edu/
Start Date
1-3-2018
End Date
1-6-2018
Description
The widespread availability of various peer-to-peer lending solutions is rapidly changing the landscape of ï¬nancial services. Beside the natural advantages over traditional services,a relevant problem in the domain is to correctly assess the risk associated with borrowers. In contrast to traditional ï¬nancial services industries, in peer-to-peer lending the unsecured nature of loans as well as the relative novelty of the platforms make the assessment of risk a difï¬cult problem. In this article we propose to use traditional machine learning methods enhanced with fuzzy set theory based transformation of data to improve the quality of identifying loans with high likelihood of default. We assess the proposed approach on a real-life dataset from one of the largest peer-to-peer platforms in Europe. The results demonstrate that (i) traditional classiï¬cation algorithms show good performance in classifying borrowers, and (ii) their performance can be improved using linguistic data transformation
Credit Risk Evaluation in Peer-to-peer Lending With Linguistic Data Transformation and Supervised Learning
Hilton Waikoloa Village, Hawaii
The widespread availability of various peer-to-peer lending solutions is rapidly changing the landscape of ï¬nancial services. Beside the natural advantages over traditional services,a relevant problem in the domain is to correctly assess the risk associated with borrowers. In contrast to traditional ï¬nancial services industries, in peer-to-peer lending the unsecured nature of loans as well as the relative novelty of the platforms make the assessment of risk a difï¬cult problem. In this article we propose to use traditional machine learning methods enhanced with fuzzy set theory based transformation of data to improve the quality of identifying loans with high likelihood of default. We assess the proposed approach on a real-life dataset from one of the largest peer-to-peer platforms in Europe. The results demonstrate that (i) traditional classiï¬cation algorithms show good performance in classifying borrowers, and (ii) their performance can be improved using linguistic data transformation
https://aisel.aisnet.org/hicss-51/da/machine_learning_in_finance/3