Financial Credit Risk Assessment Research Articles

A SC Financial Credit Risk Assessment Model Based on Particle Filter and SVM with Gain Information

The accuracy of credit risk prediction in SC financing is critical for many enterprises, based on machine learning algorithms can be good for SME credit risk assessment research, for this reason, this paper establishes a combinatorial model that can improve credit risk prediction, using support vector machine (SVM) and particle filtering to achieve credit risk classification and prediction, we and introduce information gain (IG) to extract the prediction of The model uses SVM and particle filtering to classify and predict credit risk, and we introduce information gain (IG) to extract feature variables that contribute significantly to the prediction results and optimize model feature inputs. Compared with the benchmark model, the prediction accuracy of the model in this paper is 97.62%, which is 8.97% higher than that of SVM, and the performance of IG with feature optimization improves the prediction accuracy by another 3%.

Profit vs. Equality? The Case of Financial Risk Assessment and A New Perspective on Alternative Data

The importance of pursuing financial inclusion to accelerate economic growth and enhance financial sustainability has been well noted. However, studies have provided few actionable insights into how financial institutions can balance the potential socioeconomic trade-off between profitability and equality. One major challenge arises from a lack of understanding of the impacts of various types of market information available on financial equality beyond economic profitability. Another challenge lies in how the socioeconomic trade-off under a large set of counterfactual policies in a real-world setting can be evaluated. Our motivation for the present study was the emerging sources of digitized user-behavior data (i.e., “alternative data”) stemming from the high penetration of mobile devices and internet access. Accordingly, we investigated how alternative data from smartphones and social media can help mitigate potential financial inequality while preserving business profitability in the context of financial credit risk assessment. We partnered with a leading microloan website to design a novel “meta” experiment that allowed us to simulate various real-world field experiments under an exhaustive set of counterfactual policies. Interestingly, we found that profiling user financial risk using smartphone activities is 1.3 times more effective in improving financial inclusion than using online social media information (23.05% better vs. 18.11%), and nearly 1.3 times more effective in improving business profitability (42% better vs. 33%). Surprisingly, we found that using consumers’ online shopping activities for credit risk profiling can hurt financial inclusion. Furthermore, we investigated potential explanations for financial inclusion improvements. Our findings suggest that alternative data, especially users’ smartphone activities, not only demonstrate higher ubiquity but also appear to be more orthogonal to conventional sensitive demographic attributes. This, in turn, can help mitigate statistical bias driven by the unobserved factors or underrepresentative training samples in machine-based risk assessment processes.

Internet Financial Credit Risk Assessment with Sliding Window and Attention Mechanism LSTM Model

Internet Financial Credit Risk Assessment with Sliding Window and Attention Mechanism LSTM Model

Financial credit risk assessment of online supply chain in construction industry with a hybrid model chain

Under the influence of COVID-19, although upstream small- and medium-sized enterprises (SMEs) in construction industry chain suffer from high operating costs and tight cash flow problems, their financing demands are even stronger. As an electronic and platform-based comprehensive service, online supply chain finance can ease the financing problems of SMEs in the construction industry. However, it has become an important issue for financial institutions to effectively assess the credit risk in the process of online supply chain financing. In this paper, an online supply chain risk assessment method, which is based on a hybrid model chain, including eXtreme Gradient Boosting (XGBoost), Synthetic Minority Oversampling TEchnique for Nominal and Continuous (SMOTENC), and Random Forest (RF), is proposed to identify and control the credit risk of financial institutions. Specifically, we establish the financial credit risk assessment system with respect to the characteristics of financing enterprises in the construction industry supply chain, including the status of financing enterprises, the status of core enterprises, the operating status of the supply chain, and the status of assets under financing. On the basis of the system, the best index number of the assessment system and the minority samples are obtained by the XGBoost algorithm and the SMOTENC algorithm, respectively. The classification method based on RF is applied to judge the credit risk of financing enterprises in the supply chain of construction industry. In the simulation stage, we take upstream SMEs in the supply chain of construction industry in China as an example for empirical analysis to validate the effectiveness of our proposed method. The credit risk assessment method proposed in this paper has better performance than the commonly used ones in the academic field with an average improvement on assessment accuracy for 6.39% and an average increase of Area Under Curve for 6.95%. Our study provides meaningful exploration on the fund monitoring system of the financing service platform to improve financing efficiency and risk management level.

Improved AHP Model and Neural Network for Consumer Finance Credit Risk Assessment

With the rapid expansion of the consumer financial market, the credit risk problem in borrowing has become increasingly prominent. Based on the analytic hierarchy process (AHP) and the long short-term memory (LSTM) model, this paper evaluates individual credit risk through the improved AHP and the optimized LSTM model. Firstly, the characteristic information is extracted, and the financial credit risk assessment index system structure is established. The data are input into the AHP-LSTM neural network, and the index data are fused with the AHP so as to obtain the risk level and serve as the expected output of the LSTM neural network. The results of the prewarning model after training can be used for financial credit risk assessment and prewarning. Based on LendingClub and PPDAI data sets, the experiment uses the AHP-LSTM model to classify and predict and compares it with other classification methods. Experimental results show that the performance of this method is superior to other comparison methods in both data sets, especially in the case of unbalanced data sets.

Big data driven Internet of Things for credit evaluation and early warning in finance

The big data technology framework has been successfully used in the Internet of Things, and the financial industry also hopes to use the advanced technology of big data to integrate and improve internal and external data related to credit risks. Relying on more efficient machine learning algorithms to get a reasonable prediction of credit risk can reduce the self-generated losses of the Internet of Things finance and increase profits. This article uses distributed search engine technology to customize web crawlers to obtain the required bank card and transaction data from the multi-source heterogeneous data of the Internet of Things financial industry, design the corresponding Spark parallel algorithm to preprocess the data, and establish an inverted table and two Level index file provides data source for big data analysis platform. After the data source is determined, the Mutually Exclusive Collectively Exhaustive (MECE) analysis method is combined with the scores of many financial business experts in the industry to obtain a set of candidate indicators and quantification methods for the financial credit risk evaluation of the Internet of Things, and analyze the correlation of indicators and risk grading. The random forest algorithm in the big data machine learning library is used to select the feature of the candidate index set, and a multi-level spatial association rule algorithm based on the Hash structure is designed to mine the financial risk information of the Internet of Things, and build a credit risk assessment and intelligent early warning model. This paper selects 26 indicators of Internet of Things finance as the research objects, uses SPSS26.0 software to perform sample Kaiser–Meyer–Olkin (KMO) test and Bartlett sphere test on the original data, and describes the results of factor analysis in detail. The particle swarm algorithm is introduced into the parameter optimization of random forest, and the financial credit risk assessment model of the Internet of Things is established. The results show that this method can significantly reduce the probability of banks making the first and second error rates when evaluating the credit risk of financing the Internet of Things Finance. This is conducive to the smooth development of the Internet of Things financial business for banks, which enables banks to enhance their own profitability while effectively reducing losses due to incorrect credit provision.

Profit vs. Equality? The Case of Financial Risk Assessment and A New Perspective of Alternative Data

While it is well noted the importance of pursuing inclusive finance to accelerate economic growth and financial sustainability, few actionable insights have been developed toward how financial institutions can balance the potential socioeconomic trade-off between equality and profitability. The challenges are largely three-folds: (1) lack of sufficient information (collateral); (2) lack of understanding of the impact of information on financial equality beyond economic profitability; and (3) lack of real-world causal evaluations under a (large) alternative set of counterfactual policies. Enlightened by the emerging sources of digitized user-behavior data (a.k.a. “alternative data”) enabled by the high penetration of mobile devices and internet access, we study how alternative data from smartphones and social media can help mitigate the potential financial inequality while preserving business profitability in the context of financial credit risk assessment. For evaluation, we partnered with a leading microloan platform and designed a “meta” field experiment that allowed us to recover and compare the “what-if” scenarios in the real world under an exhaustive set of counterfactual policies. Interestingly, we find that compared to using online social media information, profiling user financial risk using smartphone activities is 1.3 times more effective in improving financial inclusion (an increase of 23.05% vs. 18.11%), and likewise 1.3 times more effective in improving business profitability (an increase of 42% vs. 33%). Surprisingly, using consumers’ online shopping activities for credit risk profiling can hurt financial inclusion. Furthermore, we investigate the potential underlying mechanism for improved financial inclusion. Our findings suggest that alternative data, especially users’ smartphone activities, not only demonstrate higher ubiquity but also appear more orthogonal to conventional sensitive demographic attributes. This, in turn, can help better mitigate the statistical bias driven by unobserved factors or under-representative training samples during machine-based risk assessment processes.

Application of Fuzzy Influence Diagram Evaluation Algorithm in Supply Chain Financial Credit Risk Assessment

Application of Fuzzy Influence Diagram Evaluation Algorithm in Supply Chain Financial Credit Risk Assessment

Big data analytics on enterprise credit risk evaluation of e-Business platform

In recent years, the research on supply chain finance has been mature, in the supply chain financial risk research, the research on credit risk is mostly. However, there is little research on online supply chain finance, especially on credit risk. Therefore, this article has carried on the detailed research to the commercial bank online supply chain financial credit risk assessment. Firstly, the article applies the literature induction method to review the supply chain financial credit risk indicators, add the “online” specific indicators to supplement, combine the indicators selection principle to determine the final indicators, and construct the commercial bank online supply chain financial credit risk assessment index system, select online The supply chain financial business carried out the concentrated SMEs in the automobile manufacturing industry as the research object, using the nonlinear LS-SVM model for empirical analysis, and compared with the logistic regression model results. Secondly, the designed index system can effectively evaluate credit risk. The classification accuracy of LS-SVM evaluation model is higher than that of Logistic regression model and it has strong generalization ability. It can comprehensively identify the credit risk of small and medium-sized financing enterprises, and provide a reasonable and scientific analysis and support tool for assessing SME credit risk. Finally, combined with the fierce competition background of supply chain finance, it is proposed that commercial banks should actively carry out online supply chain finance, comprehensive risk management and deepen cooperation with e-Business platforms and logistics platforms.

An improved SMO algorithm for financial credit risk assessment – Evidence from China’s banking

With rapid development of financial services and products, credit risk assessment has recently gained considerable attention in the field of financial risk management. In this paper, an improved credit risk assessment approach is presented. Based on the credit data from China Banking Regulatory Commission (CBRC), a multi-dimensional and multi-level credit risk indicator system is constructed. In particular, we present an improved sequential minimal optimization (SMO) learning algorithm, named four-variable SMO (FV-SMO), for credit risk classification model. At each iteration, it jointly selects four variables into the working set and an theorem is proposed to guarantee the analytical solution of sub-problem. The assessment is made on China credit dataset and two benchmark credit datasets from UCI database and CD-ROM database. Experimental results demonstrate FV-SMO is competitive in saving the computational cost and outperforms other five state-of-the-art classification methods in credit risk assessment accuracy.

Financial credit risk assessment via learning-based hashing

Financial credit risk assessment via learning-based hashing

Study on Financial Credit Risk Assessment of Green Supply Chain-Taking Guangxi Listed Enterprises as an Example

This paper studies the credit risk assessment under the green supply chain financial model, selects the green credit risk evaluation index, establishes the financial credit risk evaluation model of the green supply chain, and analyzes the listed companies in Guangxi by logistic regression analysis. To provide advice and reference for enterprise financing, achieve green sustainable development of enterprises.

Study on Financial Credit Risk Assessment of Green Supply Chain-Taking Guangxi Listed Enterprises as an Example

Study on Financial Credit Risk Assessment of Green Supply Chain-Taking Guangxi Listed Enterprises as an Example

Application of a rule extraction algorithm family based on the Re-RX algorithm to financial credit risk assessment from a Pareto optimal perspective

Historically, the assessment of credit risk has proved to be both highly important and extremely difficult. Currently, financial institutions rely on the use of computer-generated credit scores for risk assessment. However, automated risk evaluations are currently imperfect, and the loss of vast amounts of capital could be prevented by improving the performance of computerized credit assessments. A number of approaches have been developed for the computation of credit scores over the last several decades, but these methods have been considered too complex without good interpretability and have therefore not been widely adopted. Therefore, in this study, we provide the first comprehensive comparison of results regarding the assessment of credit risk obtained using 10 runs of 10-fold cross validation of the Re-RX algorithm family, including the Re-RX algorithm, the Re-RX algorithm with both discrete and continuous attributes (Continuous Re-RX), the Re-RX algorithm with J48graft, the Re-RX algorithm with a trained neural network (Sampling Re-RX), NeuroLinear, NeuroLinear+GRG, and three unique rule extraction techniques involving support vector machines and Minerva from four real-life, two-class mixed credit-risk datasets. We also discuss the roles of various newly-extended types of the Re-RX algorithm and high performance classifiers from a Pareto optimal perspective. Our findings suggest that Continuous Re-RX, Re-RX with J48graft, and Sampling Re-RX comprise a powerful management tool that allows the creation of advanced, accurate, concise and interpretable decision support systems for credit risk evaluation. In addition, from a Pareto optimal perspective, the Re-RX algorithm family has superior features in relation to the comprehensibility of extracted rules and the potential for credit scoring with Big Data.

Financial credit risk assessment: a recent review

The assessment of financial credit risk is an important and challenging research topic in the area of accounting and finance. Numerous efforts have been devoted into this field since the first attempt last century. Today the study of financial credit risk assessment attracts increasing attentions in the face of one of the most severe financial crisis ever observed in the world. The accurate assessment of financial credit risk and prediction of business failure play an essential role both on economics and society. For this reason, more and more methods and algorithms were proposed in the past years. From this point, it is of crucial importance to review the nowadays methods applied to financial credit risk assessment. In this paper, we summarize the traditional statistical models and state-of-the-art intelligent methods for financial distress forecasting, with the emphasis on the most recent achievements as the promising trend in this area.

Credit risk assessment: a challenge for financial institutions

This study explores financial credit risk assessment. This is an important issue because there is currently no standardized method used by financial institutions for the assessment of credit risk. A critical evaluation of the most popular credit risk assessment methods—the judgmental method, credit-scoring and portfolio models—highlights a number of limitations when used on their own. Several interviewees confirm that credit risk assessment methods should be combined for effective credit risk assessment. Accordingly, the study proposes a framework for improving credit risk assessment, which combines the strengths of these methods and copes successfully with their limitations.