Analysis of Internet Financial Risk Control Model Based on Machine Learning Algorithms

On the basis of traditional credit risk control, this paper proposes the demand and direction of a new credit risk control strategy based on machine learning and relying on big data. First, on the basis of introducing the basic algorithmic principles of machine learning, we give reasons for choosing machine learning models and build a machine learning-based Internet consumer finance credit risk control strategy model to provide theoretical support for the empirical analysis later. Second, we take the test data of Internet consumer finance S company as the research sample and carry out empirical analysis according to the machine learning-based Internet consumer finance credit risk control strategy model. The comparison of the training results is based on the comprehensive consideration of training time, validation set accuracy, TPR evaluation indicators, and interpretability of the results; it verifies the advantages of the machine learning model in screening the key influencing factors that cause the overdue performance of credit customers. According to the optimized credit risk control strategy, corresponding strategy suggestions are provided for the credit risk control of S company. The research results show that the prediction effect of the classification model based on traditional linear regression is generally lower than that of the model based on the classification algorithm based on machine learning, and there is a complex nonlinear relationship between platform default and its related influencing factors. The accuracy of classification and early warning results of the random forest algorithm is relatively high, and the detection rate of the decision tree model is relatively high, but the cost is also the highest. In addition, the accuracy of the four types of early warning models is relatively stable, reaching an average of 80%. This paper proposes a machine learning-based Internet consumer finance credit risk control strategy model. Its system, timeliness, and risk prediction capabilities provide new ideas and suggestions for Internet consumer finance companies to design risk control strategies.

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Financial Credit Risk Control Strategy Based on Weighted Random Forest Algorithm

Scientific Programming ◽

10.1155/2021/6276155 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Guo Yangyudongnanxin

Keyword(s):

Risk Assessment ◽

Random Forest ◽

Credit Risk ◽

Control Strategy ◽

Control Strategies ◽

Risk Control ◽

Evaluation Index System ◽

Risk Level ◽

Random Forest Algorithm ◽

Financial Credit

In order to improve the effectiveness of financial credit risk control, a financial credit risk control strategy based on weighted random forest algorithm is proposed. The weighted random forest algorithm is used to classify the financial credit risk data, construct the evaluation index system, and use the analytic hierarchy process to evaluate the financial credit risk level. The targeted risk control strategies are taken according to different risk assessment results. We compared the proposed method with two other methods, and the experimental results show that the proposed method has higher classification accuracy of financial credit data and the risk assessment threshold is basically consistent with the actual results.

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Exploring energy-saving refrigerators through online e-commerce reviews: an augmented mining model based on machine learning methods

Kybernetes ◽

10.1108/k-11-2020-0788 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Yuyan Luo ◽

Zheng Yang ◽

Yuan Liang ◽

Xiaoxu Zhang ◽

Hong Xiao

Keyword(s):

Machine Learning ◽

Energy Saving ◽

Online Reviews ◽

Learning Methods ◽

Content Type ◽

Machine Learning Methods ◽

Model Based ◽

Review Mining ◽

Strategy Model ◽

Mining Model

PurposeBased on climate issues and carbon emissions, this study aims to promote low-carbon consumption and compel consumers to actively shift to energy-saving appliances. In this big data era, online reviews in social and electronic commerce (e-commerce) websites contain valuable product information, which can facilitate firm business strategies and consumer comparison shopping. This study is designed to advance existing research on energy-saving refrigerators by incorporating machine learning models in the analysis of online reviews to provide valuable suggestions to e-commerce platform managers and manufacturers to effectively understand the psychological cognition of consumers.Design/methodology/approachThis study proposes an online e-commerce review mining and management strategy model based on “data acquisition and cleaning, data mining and analysis and strategy formation” through multiple machine learning methods, namely, Bayes networks, support vector machine (SVM), latent Dirichlet allocation (LDA) and importance–performance analysis (IPA), to help managers.FindingsBased on a case study of one of the largest e-commerce platforms in China, this study linguistically analyzes 29,216 online reviews of energy-saving refrigerators. Results indicate that the energy-saving refrigerator features that consumers are generally satisfied with are, in sequential order, logistics, function, price, outlook, after-sales service, brand, quality and space. This study also identifies ten topics with 100 keywords by analyzing 18 different refrigerator models. Finally, based on the IPA, this study allocates different priorities to the features and provides suggestions from the perspective of consumers, the government and manufacturers.Research limitations/implicationsIn terms of limitations, future research may focus on the following points. First, the topics identified in this study derive from specific points in time and reviews; thus, the topics may change with the text data. A machine learning-based online review analysis platform could be developed in the future to dynamically improve consumer satisfaction. Moreover, given that consumers' needs may change over time, e-commerce platform types and consumer characteristics, such as user profiles, can be incorporated into the model to effectively analyze trends in consumers' perceived dimensions.Originality/valueThis study fills the gap in previous research in this field, which uses small-sample data for qualitative analysis, while integrating management ideas and proposes an online e-commerce review mining and management strategy model based on machine learning methods. Moreover, this study considers how consumers' emotional and thematic preferences for products affect their purchase decision-making from the perspective of their psychological perception and linguistically analyzes online reviews of energy-saving refrigerators using the proposed mining model. Through the improved IPA model, this study provides optimizing strategies to help e-commerce platform managers and manufacturers.

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A Comparative Assessment of Credit Risk Model Based on Machine Learning ——a case study of bank loan data

Procedia Computer Science ◽

10.1016/j.procs.2020.06.069 ◽

2020 ◽

Vol 174 ◽

pp. 141-149

Author(s):

Yuelin Wang ◽

Yihan Zhang ◽

Yan Lu ◽

Xinran Yu

Keyword(s):

Machine Learning ◽

Credit Risk ◽

Risk Model ◽

Comparative Assessment ◽

Bank Loan ◽

Model Based ◽

Credit Risk Model

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Improved ML-Based Technique for Credit Card Scoring in Internet Financial Risk Control

Complexity ◽

10.1155/2020/8706285 ◽

2020 ◽

Vol 2020 ◽

pp. 1-14

Author(s):

Shuangshuang Fan ◽

Yanbo Shen ◽

Shengnan Peng

Keyword(s):

Machine Learning ◽

Credit Risk ◽

Credit Card ◽

Financial Risk ◽

Risk Control ◽

Assessment Model ◽

Complex Data ◽

Continuous Growth ◽

Scoring Model ◽

Extreme Gradient Boosting

With the rapid development of China’s Internet finance industry and the continuous growth of transaction amount in recent years, a variety of financial risks have increased, especially credit risk in the financial industry. Also, the credit risk evaluation is usually made by using the application card scoring model, which has the shortcomings of strict data assumption and inability to process complex data. In order to overcome the limitations of the credit card scoring model and evaluate credit risk better, this paper proposes a credit evaluation model based on extreme gradient boosting tree (XGBoost) machine learning (ML) algorithm to construct a credit risk assessment model for Internet financial institutions. At the same time, an Internet lending company in China is taken as a case study to compare the performance of the traditional credit card scoring model and the proposed machine learning (ML) algorithm model. The results show that ML algorithm has a very significant advantage in the field of Internet financial risk control, it has more accurate prediction results and has no particularly strict assumptions and restrictions on data, and the process of processing data is more convenient and reliable. We should increase the application of ML in the field of financial risk control. The value of this paper lies in enriching the related research of financial technology and providing a new reference for the practice of financial risk control.

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Research on Pump Inspection Cycle Early Warning Method Based on Big Data

E3S Web of Conferences ◽

10.1051/e3sconf/202124502026 ◽

2021 ◽

Vol 245 ◽

pp. 02026

Author(s):

Du Lihong ◽

Liu Yufang ◽

Cao Fei ◽

Li Fang ◽

Min Guizhi ◽

...

Keyword(s):

Machine Learning ◽

Big Data ◽

Influencing Factors ◽

Early Warning ◽

Learning Technology ◽

Inspection Period ◽

Engineering Technology ◽

Early Warning Model ◽

Single Well ◽

Warning Model

At present, the existing indicator diagram can only be used for expost judgment and can not give early warning, and the influencing factors of pump inspection period are nonlinear, multi constrained and multi variable. In this paper, big data machine learning method is used to carry out relevant research. Firstly, around the influencing factors of pump inspection cycle, relevant data are collected and the evaluation index of pump inspection cycle is designed. Then, based on feature engineering technology, the production parameters of oil wells in different pump inspection periods are calculated to form the analysis sample set of pump inspection period. Finally, the early warning model of pump inspection period is established by using machine learning technology. The experimental results show that: the pump inspection cycle early warning model established by stochastic forest algorithm can identify the pump inspection status of single well, and the accuracy rate is about 85%.

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Credit Risk Model Based on Central Bank Credit Registry Data

Journal of Risk and Financial Management ◽

10.3390/jrfm14030138 ◽

2021 ◽

Vol 14 (3) ◽

pp. 138

Author(s):

Fisnik Doko ◽

Slobodan Kalajdziski ◽

Igor Mishkovski

Keyword(s):

Machine Learning ◽

Random Forest ◽

Decision Tree ◽

Central Bank ◽

Credit Risk ◽

Commercial Banks ◽

Registry Data ◽

Learning Models ◽

Model Based ◽

Machine Learning Models

Data science and machine-learning techniques help banks to optimize enterprise operations, enhance risk analyses and gain competitive advantage. There is a vast amount of research in credit risk, but to our knowledge, none of them uses credit registry as a data source to model the probability of default for individual clients. The goal of this paper is to evaluate different machine-learning models to create accurate model for credit risk assessment using the data from the real credit registry dataset of the Central Bank of Republic of North Macedonia. We strongly believe that the model developed in this research will be an additional source of valuable information to commercial banks, by leveraging historical data for all the population of the country in all the commercial banks. Thus, in this research, we compare five machine-learning models to classify credit risk data, i.e., logistic regression, decision tree, random forest, support vector machines (SVM) and neural network. We evaluate the five models using different machine-learning metrics, and we propose a model based on credit registry data from the central bank with detailed methodology that can predict the credit risk based on credit history of the population in the country. Our results show that the best accuracy is achieved by using decision tree performing on imbalanced data with and without scaling, followed by random forest and linear regression.

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