Low rank representation and discriminant analysis-based models for peer-to-peer default risk assessment

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Gui Yuan ◽  
Shali Huang ◽  
Jing Fu ◽  
Xinwei Jiang
Keyword(s):  
Risk Assessment ◽  
Discriminant Analysis ◽  
Default Risk ◽  
Assessment Model ◽  
Low Rank ◽  
Analysis Model ◽  
Content Type ◽  
Model Based ◽  
Low Rank Representation ◽  
Online Lending

Purpose This study aims to assess the default risk of borrowers in peer-to-peer (P2P) online lending platforms. The authors propose a novel default risk classification model based on data cleaning and feature extraction, which increases risk assessment accuracy. Design/methodology/approach The authors use borrower data from the Lending Club and propose the risk assessment model based on low-rank representation (LRR) and discriminant analysis. Firstly, the authors use three LRR models to clean the high-dimensional borrower data by removing outliers and noise, and then the authors adopt a discriminant analysis algorithm to reduce the dimension of the cleaned data. In the dimension-reduced feature space, machine learning classifiers including the k-nearest neighbour, support vector machine and artificial neural network are used to assess and classify default risks. Findings The results reveal significant noise and redundancy in the borrower data. LRR models can effectively clean such data, particularly the two LRR models with local manifold regularisation. In addition, the supervised discriminant analysis model, termed the local Fisher discriminant analysis model, can extract low-dimensional and discriminative features, which further increases the accuracy of the final risk assessment models. Originality/value The originality of this study is that it proposes a novel default risk assessment model, based on data cleaning and feature extraction, for P2P online lending platforms. The proposed approach is innovative and efficient in the P2P online lending field.

Research on Intrusion Detection Method Based on Neural Network

2011 ◽  
Vol 403-408 ◽  
pp. 1483-1486
Author(s):  
Chi Xu ◽  
Wen Fang Zhang
Keyword(s):  
Risk Assessment ◽  
Network Security ◽  
Security Analysis ◽  
Assessment Model ◽  
Security Assessment ◽  
Security Risk ◽  
Analysis Model ◽  
Model Based ◽  
Security Risk Assessment ◽  
Meaningful Definition

The research of this dissertation focuses on the quantitative methodologies of the network security assessment. So the dissertation gives the term “Model-based network security risk assessment” a more meaningful definition. The methodology called a model-based will use one network security analysis model to design the scenarios of threatens according to the Patterns behaviors of vulnerabilities and threatens in the network. And then the risk assessment will be done under those scenarios. Following this new definition. The dissertation Proposes a Component-centric Access Graph Based Network Security Risk Assessment Model (Oc-AGNSRAM).

scholarly journals Big Data Analytics for Complex Credit Risk Assessment of Network Lending Based on SMOTE Algorithm

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Aiwen Niu ◽  
Bingqing Cai ◽  
Shousong Cai
Keyword(s):  
Risk Assessment ◽  
Big Data ◽  
Credit Risk ◽  
Big Data Analytics ◽  
Assessment Model ◽  
Effective Control ◽  
Credit Score ◽  
Credit Risk Assessment ◽  
Index Weight ◽  
Online Lending

With the continuous development of big data technology, the data of online lending platform witness explosive development. How to give full play to the advantages of data, establish a credit risk assessment model, and realize the effective control of platform credit risk have become the focus of online lending platform. In view of the fact that the network loan data are mainly unbalanced data, the smote algorithm is helpful to optimize the model and improve the evaluation performance of the model. Relevant research shows that stochastic forest model has higher applicability in credit risk assessment, and cart, ANN, C4.5, and other algorithms are also widely used. In the influencing factors of credit evaluation, the weight of the applicant’s enterprise scale, working years, historical records, credit score, and other indicators is relatively high, while the index weight of marriage and housing/car production (loan) is relatively low.

Dynamic safety prewarning mechanism of human–machine–environment using computer vision

2020 ◽  
Vol 27 (8) ◽  
pp. 1813-1833 ◽  
Author(s):  
Wenpei Xu ◽  
Ting-Kwei Wang
Keyword(s):  
Risk Assessment ◽  
Computer Vision ◽  
Expert Knowledge ◽  
Safety Information ◽  
Assessment Model ◽  
Risk Indicators ◽  
Risk Assessment Model ◽  
Content Type ◽  
Risk Levels

PurposeThis study provides a safety prewarning mechanism, which includes a comprehensive risk assessment model and a safety prewarning system. The comprehensive risk assessment model is capable of assessing nine safety indicators, which can be categorised into workers’ behaviour, environment and machine-related safety indicators, and the model is embedded in the safety prewarning system. The safety prewarning system can automatically extract safety information from surveillance cameras based on computer vision, assess risks based on the embedded comprehensive risk assessment model, categorise risks into five levels and provide timely suggestions.Design/methodology/approachFirstly, the comprehensive risk assessment model is constructed by adopting grey multihierarchical analysis method. The method combines the Analytic Hierarchy Process (AHP) and the grey clustering evaluation in the grey theory. Expert knowledge, obtained through the questionnaire approach, contributes to set weights of risk indicators and evaluate risks. Secondly, a safety prewarning system is developed, including data acquisition layer, data processing layer and prewarning layer. Computer vision is applied in the system to automatically extract real-time safety information from the surveillance cameras. The safety information is then processed through the comprehensive risk assessment model and categorized into five risk levels. A case study is presented to verify the proposed mechanism.FindingsThrough a case study, the result shows that the proposed mechanism is capable of analyzing integrated human-machine-environment risk, timely categorising risks into five risk levels and providing potential suggestions.Originality/valueThe comprehensive risk assessment model is capable of assessing nine risk indicators, identifying three types of entities, workers, environment and machine on the construction site, presenting the integrated risk based on nine indicators. The proposed mechanism, which adopts expert knowledge through Building Information Modeling (BIM) safety simulation and extracts safety information based on computer vision, can perform a dynamic real-time risk analysis, categorize risks into five risk levels and provide potential suggestions to corresponding risk owners. The proposed mechanism can allow the project manager to take timely actions.

Detection of varied defects in diverse fabric images via modified RPCA with noise term and defect prior

2016 ◽  
Vol 28 (4) ◽  
pp. 516-529 ◽  
Author(s):  
Junjie Cao ◽  
Nannan Wang ◽  
Jie Zhang ◽  
Zhijie Wen ◽  
Bo Li ◽  
...  
Keyword(s):  
Principal Component ◽  
Recovery Process ◽  
Low Rank ◽  
Analysis Model ◽  
Content Type ◽  
Robust Detection ◽  
Matrix Recovery ◽  
Noise Term ◽  
The Matrix ◽  
Rank Defect

Purpose – The purpose of this paper is to present a novel method for fabric defect detection. Design/methodology/approach – The method based on joint low-rank and spare matrix recovery, since patterned fabric is manufactured by a set of predefined symmetry rules, and it can be seen as the superposition of sparse defective regions and low-rank defect-free regions. A robust principal component analysis model with a noise term is designed to handle fabric images with diverse patterns robustly. The authors also estimate a defect prior and use it to guide the matrix recovery process for accurate extraction of various fabric defects. Findings – Experiments on plain and twill, dot-, box- and star-patterned fabric images with various defects demonstrate that the method is more efficient and robust than previous methods. Originality/value – The authors present a RPCA-based model for fabric defects detection, and show how to incorporate defect prior to improve the detection results. The authors also show that more robust detection and less running time can be obtained by introducing a noise term into the model.

scholarly journals Credit Risk Assessment Models of Retail Microfinancing: The Case of a Malaysian National Savings Bank’s Branch

2020 ◽  
Vol 11 (3) ◽  
pp. 73
Author(s):  
Mazni Asrida Abdullah ◽  
Azlina Ahmad ◽  
Nor Azam Mat Nayan ◽  
Zubir Azhar ◽  
Abd-Razak Ahmad
Keyword(s):  
Risk Assessment ◽  
Default Risk ◽  
Credit Rating ◽  
Predictive Ability ◽  
Assessment Model ◽  
Accuracy Rate ◽  
Credit Score ◽  
Factors Associated ◽  
Risk Assessment Models ◽  
New Information

Internal ratings have been used by banks to evaluate the creditworthiness of their borrowers with diverse practices. This research aims to analyse the practice of assessing (or predicting) the credit performance of microfinancing loans of a Malaysian bank and to suggest how the existing performance of credit assessment model can be improved. Logistic regression was used to investigate the predictive ability of information on business operators’ management and accounting skills as factors to predict default risk of borrowers. The combination of these information formed the three (3) models that were used in the analysis. The accuracy rate of each model was then measured. A sample of respondents was selected among microfinance borrowers in a national savings bank’s branch in Malaysia. A total of 106 questionnaires were used for data analysis. The findings suggest that good credit rating, business experience, business financial and forecasting capability are factors associated with whether SMEs will default or not in their payments. The combination of credit score used currently by the bank and the new information produced by this research increases the bank’s ability to predict default.

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