P2P Lending Default Prediction Based on AI and Statistical Models

Peer-to-peer lending (P2P lending) has proliferated in recent years thanks to Fintech and big data advancements. However, P2P lending platforms are not tightly governed by relevant laws yet, as their development speed has far exceeded that of regulations. Therefore, P2P lending operations are still...

Full description

Saved in:
Bibliographic Details
Published in:Entropy (Basel, Switzerland) Switzerland), 2022-06, Vol.24 (6), p.801
Main Authors: Ko, Po-Chang, Lin, Ping-Chen, Do, Hoang-Thu, Huang, You-Fu
Format: Article
Language:English
Subjects:
Accuracy
AI model
Artificial neural networks
Asymmetry
Bank technology
Big Data
Classification
Credit scoring
Data analysis
data processing
Datasets
Debt
Decision analysis
Decision making
Decision trees
Default
Discriminant analysis
Empirical analysis
Feature selection
Financial institutions
Literature reviews
Loans
P2P lending default prediction
Peer to peer lending
Platforms
Prediction models
Statistical analysis
statistical model
Statistical models
Statistical prediction
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Peer-to-peer lending (P2P lending) has proliferated in recent years thanks to Fintech and big data advancements. However, P2P lending platforms are not tightly governed by relevant laws yet, as their development speed has far exceeded that of regulations. Therefore, P2P lending operations are still subject to risks. This paper proposes prediction models to mitigate the risks of default and asymmetric information on P2P lending platforms. Specifically, we designed sophisticated procedures to pre-process mass data extracted from Lending Club in 2018 Q3–2019 Q2. After that, three statistical models, namely, Logistic Regression, Bayesian Classifier, and Linear Discriminant Analysis (LDA), and five AI models, namely, Decision Tree, Random Forest, LightGBM, Artificial Neural Network (ANN), and Convolutional Neural Network (CNN), were utilized for data analysis. The loan statuses of Lending Club’s customers were rationally classified. To evaluate the models, we adopted the confusion matrix series of metrics, AUC-ROC curve, Kolmogorov–Smirnov chart (KS), and Student’s t-test. Empirical studies show that LightGBM produces the best performance and is 2.91% more accurate than the other models, resulting in a revenue improvement of nearly USD 24 million for Lending Club. Student’s t-test proves that the differences between models are statistically significant.
ISSN:1099-4300
1099-4300
DOI:10.3390/e24060801