Research and Design of Credit Risk Assessment System Based on Big Data and Machine Learning

2021 ◽  
Author(s):  
Song Wen ◽  
Bi Zeng ◽  
Wenxiong Liao ◽  
Pengfei Wei ◽  
Zhihao Pan
Keyword(s):  
Machine Learning ◽  
Risk Assessment ◽  
Big Data ◽  
Credit Risk ◽  
Assessment System ◽  
Credit Risk Assessment ◽  
Research And Design

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.

Machine learning-based classifiers ensemble for credit risk assessment

2013 ◽  
Vol 7 (3/4) ◽  
pp. 227 ◽  
Author(s):  
Trilok Nath Pandey ◽  
Alok Kumar Jagadev ◽  
D. Choudhury ◽  
Satchidananda Dehuri
Keyword(s):  
Machine Learning ◽  
Risk Assessment ◽  
Credit Risk ◽  
Credit Risk Assessment

scholarly journals Credit Risk Assessment using Machine Learning Techniques

2019 ◽  
Vol 9 (1) ◽  
pp. 3482-3486
Keyword(s):  
Machine Learning ◽  
Risk Assessment ◽  
Random Forest ◽  
Credit Risk ◽  
Banking Sector ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Credit Risk Assessment ◽  
Learning Techniques ◽  
Cart Algorithm

Analysis of credit scoring is an effective credit risk assessment technique, which is one of the major research fields in the banking sector. Machine learning has a variety of applications in the banking sector and it has been widely used for data analysis. Modern techniques such as machine learning have provided a self-regulating process to analyze the data using classification techniques. The classification method is a supervised learning process in which the computer learns from the input data provided and makes use of this information to classify the new dataset. This research paper presents a comparison of various machine learning techniques used to evaluate the credit risk. A credit transaction that needs to be accepted or rejected is trained and implemented on the dataset using different machine learning algorithms. The techniques are implemented on the German credit dataset taken from UCI repository which has 1000 instances and 21 attributes, depending on which the transactions are either accepted or rejected. This paper compares algorithms such as Support Vector Network, Neural Network, Logistic Regression, Naive Bayes, Random Forest, and Classification and Regression Trees (CART) algorithm and the results obtained show that Random Forest algorithm was able to predict credit risk with higher accuracy

scholarly journals Risk Assessment of Internet Credit Based on Big Data Analysis

2020 ◽  
Vol 214 ◽  
pp. 01012
Author(s):  
WANG HAORU ◽  
Yi Zhixuan ◽  
WEI YUJIA ◽  
Tianpeng Yao ◽  
Zhao Shuoheng ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Big Data ◽  
Data Analysis ◽  
Credit Risk ◽  
Commercial Banks ◽  
Big Data Analysis ◽  
Financial Fraud ◽  
Credit Risk Assessment ◽  
New Business ◽  
Internet Finance

In recent years, network technology has continued to develop, and Internet finance has rapidly developed into a new business area. Internet credit is one of the important ways for banks to conduct business, and the scale of online credit has continued to expand. Due to the existence of various unpredictable factors, frequent emergencies, and online financial fraud, the overall market risk in the field of online credit has increased, and the rate of non-performing loans has continued to increase. Online financial fraud cases show that online credit risk has become one of the most prominent risks in the operation of commercial banks, which has a direct impact on the stability and development of commercial banks. We can build a bank database system based on big data, introduce professional big data analysis technical personnel, and constantly improve the big data sharing analysis platform, so that commercial banks can use system data more fully and effectively, and facilitate relevant business personnel to use big data technology for analysis and calculation. Big data is constantly produced, which provides basic materials for online credit risk assessment. Big data analysis technology is gradually mature, and it has the necessary conditions for online credit risk assessment. Based on the theories and technologies related to big data analysis, this paper comprehensively evaluates the online credit risk in the form of example data analysis, thereby effectively reducing the online credit risk coefficient.

scholarly journals Application of Machine Learning in Credit Risk Assessment: A Prelude to Smart Banking

2019 ◽  
Author(s):  
Syed Zamil Hasan Shoumo ◽  
Mir Ishrak Maheer Dhruba ◽  
Sazzad Hossain ◽  
Nawab Haider Ghani ◽  
Hossain Arif ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Assessment ◽  
Credit Risk ◽  
Credit Risk Assessment

scholarly journals The Risk Management of Commercial Banks——Credit-Risk Assessment of Enterprises

2016 ◽  
Vol 8 (9) ◽  
pp. 69
Author(s):  
Na Luo ◽  
Jiayi Yang ◽  
Yuanfeng Zhu ◽  
Yu Zhang
Keyword(s):  
Risk Assessment ◽  
Risk Management ◽  
Credit Risk ◽  
Commercial Banks ◽  
Assessment System ◽  
Assessment Model ◽  
Good Prediction ◽  
Credit Risk Management ◽  
Credit Risk Assessment ◽  
Credit Risks

With the diversified developments of the financial market, commercial banks are confronted with various risks, among which the credit risk is the core, and thus the assessment of enterprises’ credit risks is especially important in the credit process of the commercial banks. Based on the relevant researches about commercial banks’ credit risk management, the paper carries out a deep analysis on the factors that may affect the credit risk assessment and then establishes a relatively comprehensive credit risk assessment system. In this paper, we apply our risk assessment model, which is established on the basis of GRNN neural network model, to make an empirical analysis with the selected sample data. And the results suggest that the hit rates of identifying high quality enterprises and low quality enterprises are 92.16 percent and 93.75 percent, respectively, indicating that the model has realized a good prediction.

Sign in / Sign up

Export Citation Format

Share Document