Predicting repayment of borrows in peer‐to‐peer social lending with deep dense convolutional network

Kim, Ji Yoon; Cho, Sung Bae

doi:10.1111/exsy.12403

Cited by 27 publications

(11 citation statements)

References 39 publications

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“…Among them, N 1 , N R , and N s , respectively, represent the average mutual information feature quantity and state distribution set of cloud service portfolio big data [ 11 ].…”

Section: Distributed Structure Model and Feature Extraction Of Big Datamentioning

confidence: 99%

Big Data Analysis and Prediction System Based on Improved Convolutional Neural Network

Cao

Zhang

2022

Computational Intelligence and Neuroscience

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This paper presents a big data analysis and prediction system based on convolutional neural networks. Continuous template matching technology is used to analyze the distributed data structure of big data, and the information fusion processing of cloud service combination big data is combined with matching related detection methods, frequent item detection, and association rule feature extraction of high-dimensional fusion data. A clustering method is adopted to realize the classification and mining of cloud service portfolio big data. The hardware equipment of the car to detect the surrounding environment is complicated, and the combination of the convolutional neural network and the camera to detect the surrounding environment has become a research hotspot. However, simply using the convolutional neural network to process the camera data to control the turning angle of the car has the problems of long training time and low accuracy. An improved convolutional neural network is proposed. The experimental results show that the accuracy of data mining by this method is 12.43% and 21.76% higher than that of traditional methods, and the number of iteration steps is shorter, indicating that the timeliness of mining is higher. This network structure can effectively improve the training speed of the network and improve the accuracy of the network. It is proven that the convolutional neural network has faster training speed and higher accuracy.

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“…Among them, N 1 , N R , and N s , respectively, represent the average mutual information feature quantity and state distribution set of cloud service portfolio big data [ 11 ].…”

Section: Distributed Structure Model and Feature Extraction Of Big Datamentioning

confidence: 99%

Big Data Analysis and Prediction System Based on Improved Convolutional Neural Network

Cao

Zhang

2022

Computational Intelligence and Neuroscience

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“…In [13], a random forest based classification approach was used to identify the loan status and it turned out the random forest model could reach a higher accuracy than support vector machine or logistic regression. In [9], a deep dense convolutional network was created to predict the repayment amount of P2P lending. Tree-based ensemble algorithms including lightGBM and XGBoost methods have been used to evaluate the loans on the Lending Club platform as well [12].…”

Section: Research On Credit Scoringmentioning

confidence: 99%

Improving Investment Suggestions for Peer-to-Peer Lending via Integrating Credit Scoring into Profit Scoring

Wang

2020

Proceedings of the 2020 ACM Southeast Conference

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In the peer-to-peer (P2P) lending market, lenders lend the money to the borrowers through a virtual platform and earn the possible profit generated by the interest rate. From the perspective of lenders, they want to maximize the profit while minimizing the risk. Therefore, many studies have used machine learning algorithms to help the lenders identify the "best" loans for making investments. The studies have mainly focused on two categories to guide the lenders' investments: one aims at minimizing the risk of investment (i.e., the credit scoring perspective) while the other aims at maximizing the profit (i.e., the profit scoring perspective). However, they have all focused on one category only and there is seldom research trying to integrate the two categories together. Motivated by this, we propose a two-stage framework that incorporates the credit information into a profit scoring modeling. We conducted the empirical experiment on a real-world P2P lending data from the US P2P market and used the Light Gradient Boosting Machine (lightGBM) algorithm in the two-stage framework. Results show that the proposed two-stage method could identify more profitable loans and thereby provide better investment guidance to the investors compared to the existing one-stage profit scoring alone approach. Therefore, the proposed framework serves as an innovative perspective for making investment decisions in P2P lending. CCS CONCEPTS • Computer systems organization → Machine learning; Modeling.

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“…Such a topic is addressed in the paper by J.‐Y. Kim and Cho () that proposes a deep dense convolutional network for repayment prediction in social lending. It maintains the borrower's semantic information and obtains a good representation by automatically extracting important low‐ and high‐level features simultaneously.…”

Section: Contents Of the Special Issuementioning

confidence: 99%

Soft computing applications in the field of industrial and environmental enterprises

Jiménez

Herrero

2019

Expert Systems

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Predicting repayment of borrows in peer‐to‐peer social lending with deep dense convolutional network

Cited by 27 publications

References 39 publications

Big Data Analysis and Prediction System Based on Improved Convolutional Neural Network

Big Data Analysis and Prediction System Based on Improved Convolutional Neural Network

Improving Investment Suggestions for Peer-to-Peer Lending via Integrating Credit Scoring into Profit Scoring

Soft computing applications in the field of industrial and environmental enterprises

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