Research on the framework construction of the big data algorithm in the financial credit investigation system

2021 ◽  
Author(s):  
Honglin Fu
Keyword(s):  
Big Data ◽  
Financial Credit

scholarly journals Give us a little social credit: to design or to discover personal ratings in the era of Big Data

2020 ◽  
Vol 16 (3) ◽  
pp. 369-387 ◽  
Author(s):  
Abigail Devereaux ◽  
Linan Peng
Keyword(s):  
Big Data ◽  
Credit Rating ◽  
Credit Ratings ◽  
Personal Characteristics ◽  
The State ◽  
State Council ◽  
Credit System ◽  
Personal Credit ◽  
Social Credit ◽  
Financial Credit

AbstractIn 2014, the State Council of the Chinese Communist Party announced the institution of a social credit system by 2020, a follow-up to a similar statement on the creation of a social credit system issued by the State Council in 2007. Social credit ratings of the type being developed by the State Council in partnership with Chinese companies go beyond existing financial credit ratings in an attempt to project less-tangible personal characteristics like trustworthiness, criminal tendencies, and group loyalty onto a single scale. The emergence of personal credit ratings is enabled by Big Data, automated decision-making processes, machine learning, and facial recognition technology. It is quite likely that various kinds of personal and social credit ratings shall become reality in the near future. We explore China's version of its social credit system so far, compare the welfare and epistemological qualities of an ecology of personal ratings emanating from polycentric sources versus a social credit rating, and discuss whether a social credit system in an ideologically driven state is less a tool to maximize social welfare through trustworthiness provision and more a method of preventing and punishing deviance from a set of party-held ideological values.

scholarly journals Challenges of Credit Reference Based on Big Data Technology in China

2021 ◽  
Author(s):  
Cheng-yong Liu ◽  
Chih-Chun Hou
Keyword(s):  
Big Data ◽  
Information Security ◽  
Secure Communication ◽  
Distributed Storage ◽  
Blockchain Technology ◽  
Financial Credit ◽  
Credit Data ◽  
Point To Point ◽  
The Individual ◽  
Big Data Technology

AbstractBig data-based credit reference system gradually attracts wide attention due to its ad-vantages in remedying the shortages of traditional credit reference and dealing with new challenges arising from financial credit management. Nevertheless, this new method is also adapted through different studies and experiments to be problematic with island of credit information and information security. Some researchers begin exploring the possibility of applying blockchain technology to the individual credit reference field. The business links in the individual credit reference can be innovated through the blockchain mechanism so that credit data from different industries get collected through peering points, secure communication and anonymous protection on the basis of such techniques as distributed storage, point-to-point transmission, consensus mechanism and encryption algorithm. In this way, it is feasible to solve island of information and enhance the protection of user information security. A promising future can be expected about the big data-based credit reference, but there are also many problems with blockchain-based credit reference in China.

scholarly journals Research on Application of Big Data in Internet Financial Credit Investigation Based on Improved GA-BP Neural Network

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Fei-Peng Wang
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Bp Neural Network ◽  
Adaptive Genetic Algorithm ◽  
Accuracy Rate ◽  
Financial Credit ◽  
Internet Finance ◽  
Tracking Model ◽  
Credit Data ◽  
Research On Application

The arrival of the era of big data has provided a new direction of development for internet financial credit collection. First of all, the article introduced the situation of internet finance and traditional credit industry. Based on that, the mathematical model was used to demonstrate the necessity of developing big data financial credit information. Then, the Internet financial credit data are preprocessed, the variables suitable for modeling are selected, and the dynamic credit tracking model of BP neural network based on adaptive genetic algorithm is constructed. It is found that both LM training algorithm and Bayesian algorithm can converge the error to 10e-6 quickly in the model training, and the overall training effect is ideal. Finally, the rule extraction algorithm is used to simulate the test samples. The accuracy rate of each sample method is over 90%, and some accuracy rate is even more than 90%, which indicates that the model is applicable to the credit data of big data in internet finance.

scholarly journals Fine clustering analysis of Internet financial credit investigation based on big data

2021 ◽  
pp. 100297
Author(s):  
Jingqi Sun ◽  
Yu Li ◽  
Qiang Li ◽  
Yingji Li ◽  
Yanshu Jia ◽  
...  
Keyword(s):  
Big Data ◽  
Clustering Analysis ◽  
Financial Credit

scholarly journals Research on Credit Risk Identification of Internet Financial Enterprises Based on Big Data

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Hua Peng
Keyword(s):  
Big Data ◽  
Credit Risk ◽  
Learning Algorithm ◽  
Synthesis Method ◽  
Risk Identification ◽  
Gradient Boosting ◽  
Analytic Hierarchy ◽  
Region Division ◽  
Extreme Gradient Boosting ◽  
Financial Credit

The advent of the era of big data has provided a new way of development for Internet financial credit collection. The traditional methods of credit risk identification of Internet financial enterprises cannot get the characteristics of credit risk zoning, leading to large errors in the results of credit risk identification. Therefore, this paper proposes a new method of credit risk identification based on big data for Internet financial enterprises. According to the big data perspective, the credit risk assessment steps of Internet financial enterprises are analyzed and the weight of assessment indicators is calculated using the improved analytic hierarchy process (AHP), and the linear weighted synthesis method is applied to comprehensively assess the credit of clients. Using the unique characteristics of big data credit risk region division, the big data credit risk is determined by rule-based matching method. The eXtreme Gradient Boosting (XGBoost) machine learning algorithm is used to establish a credit risk identification model of Internet financial enterprises. The kappa coefficient and ROC curve are used to evaluate the performance of the proposed method. Experimental results show that the proposed method can accurately assess the credit risk of Internet financial enterprises.

scholarly journals Study on Internet Finance Credit Information Sharing Based on Block Chain Technology

2018 ◽  
Vol 14 (2) ◽  
pp. 81 ◽  
Author(s):  
Maoran Zhu ◽  
Xin Liu
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Data Privacy ◽  
Distributed Database ◽  
Financial Credit ◽  
User Data ◽  
Block Chain ◽  
Distributed Database System ◽  
Credit Data ◽  
Big Data Technology

With development of Big Data technology these years, Internet financial companies in China started trying using big data technology to do credit investigation instead of traditional methods. But there is some limitation and problem in terms of data acquisition channel, information asymmetry and data privacy protection, etc. Block chain, characterized in unalterability and decentralization comes into people's sight. This paper will introduce block chain technology, explore the use of block chain technology in Internet financial credit investigation, and put forward an internet financial credit data sharing model based on block chain, which mainly composed by the Fin-tech Federate Servers group (FFS), the user data storage structure and a distributed database system (DDBS). By combining DPoS and re-encryption technology, the model has the characteristics of non-tampering, authorized access and convenient accountability. Through this model, the user data is recorded by the trusted agent, encrypted by asymmetric encryption technology, and anchored to the chain of the block periodically.

Internet Financial Credit Evaluation for the Small and Medium Enterprises

2014 ◽  
Vol 989-994 ◽  
pp. 5075-5077
Author(s):  
Yi Qing Lu
Keyword(s):  
Big Data ◽  
Credit Risk ◽  
Information Asymmetry ◽  
Financial Institutions ◽  
Evaluation System ◽  
Small And Medium Enterprises ◽  
Credit Evaluation ◽  
Financial Credit ◽  
Medium Enterprises ◽  
Credit Evaluation System

In this paper, a credit evaluation system based big-data is designed to change the information asymmetry between the finance institutions and enterprises, reduce the credit risk of internet financial institutions and investors, by utilizing the information and technology advantages. The research objective of this project have important theoretical and application value to the development of small and medium-sized enterprises (SME) credit evaluation system.

scholarly journals Prevention of Internet Financial Risks in the Era of Digital Economy

2021 ◽  
Vol 4 (5) ◽  
pp. 38-44
Author(s):  
Yan Wang
Keyword(s):  
Big Data ◽  
Digital Economy ◽  
The Internet ◽  
Financial Risks ◽  
Financial Industry ◽  
Widespread Application ◽  
Credit Reporting ◽  
Financial Credit ◽  
Internet Finance ◽  
The Times

As a pillar in the development of China’s economy, the financial industry plays a key role in the production and life of residents. Along with the widespread application of the internet, internet finance has gradually emerged as required by the times, and in the achievement of the collection and extraction of big data, related analysis and exploration technologies have been emphasized more. However, in the context of big data technology, there are still risks of unsound laws, inadequate business publicity, user information security, and capital liquidity in internet finance. Under this digital economy era, this article attempts to discuss these risks, which need to be prevented from establishing a good internet financial system, strengthening interindustry exchanges and cooperation, building a unified internet financial information supervision platform, as well as optimizing the internet financial credit reporting system, so as to promote a healthy and sound development of the whole financial industry.

Find Out About 'Big Data' to Track Outcomes

ASHA Leader ◽  
2013 ◽  
Vol 18 (2) ◽  
pp. 59-59
Keyword(s):  
Big Data

Find Out About 'Big Data' to Track Outcomes

Sign in / Sign up

Export Citation Format

Share Document