A New Legal Design for the Credit Reporting Industry

Under the traditional and well-established conception, credit bureaus are understood as being little more than vessels storing customer data that has been furnished by specific suppliers, such as banks, intermediaries and, more generally, lenders. Accordingly, credit bureaus are deemed to play a ‘neutral’ role in the credit market. Hence, in Europe and the US, they are generally not responsible for any inaccuracies in the information they put into circulation, despite data quality being crucial for the proper functioning of the market and for a fair allocation of resources. This circumstance engenders, however, a conflict between the market interest in accurate data and the ‘endogenous’ for-profit interest of credit bureaus. More specifically, despite their performing an important activity in an oligopolistic environment, credit bureaus are presently allowed to pursue their for-profit interest without any substantial accountability as regards their ‘exogenous’ function of transmitting accurate data. This disequilibrium oftentimes results in under-performance and a low level of data quality. The analysis points out, also through a comparative analysis with the rules governing the US credit report system, how these circumstances in the credit market sector can imperil some pivotal objectives of the EU legal policy, which aims at ensuring equal levels of protection among citizens of Member States within a unique environment dependent on the cross-border exchange of information.

Prevention of Internet Financial Risks in the Era of Digital Economy

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.

Algorithm Transparency through the Fair Credit Reporting Act (FCRA)

Racial discrimination in housing has long fueled disparities in homeownership and wealth in the United States. Now, automated algorithms play a dominant role in rental and lending decisions. Advocates of these technologies argue that mortgage lending algorithms reduce discrimination. However, “errors in background check reports persist and remain pervasive,” and algorithms are at risk for inheriting prejudices from society and reflect pre-existing patterns of inequality. Additionally, algorithmic discrimination is often challenging to identify and difficult to explain or prosecute in court. While the Federal Trade Commission (FTC) is responsible for prosecuting this type of discrimination under the Fair Credit Reporting Act (FCRA), their enforcement regime “has inadequately regulated industry at the federal and state level and failed to provide consumers access to justice at an individual level,” as evidenced by its mere eighty-seven enforcement actions in the past forty years. In comparison, 4,531 lawsuits have been brought under the FCRA by other groups in 2018 alone. Therefore, the FTC must update its policies to ensure it can identify, prosecute, and facilitate third-party lawsuits against a primary driver of housing discrimination in the 21st century: discrimination within algorithmic decision making. We recommend that the FTC issue a rule requiring companies to publish a data plan with all consumer reporting products. Currently, the FTC recommends that companies make an internal assessment of the components of the proposed data plan to ensure that they are not in violation of the FCRA. Therefore, requiring that these plans be published publicly does not place undue burden on companies and empowers consumers to advocate for themselves and report unfair practices to the FTC. Coupled together, these will reduce the costs of investigation and enforcement by the FTC and decrease the discriminatory impact of automated decision systems on marginalized communities.

Research on the Challenges and Sustainable Development Strategies of Community Banks in China

With the rapid development of information technology and the Internet, online financial services are fast, equal, and flexible, which bring a lot of challenges to commercial banks, especially the subsidiary community banks. This thesis first clarifies related research at home and abroad and further expounds on the development status and existing problems of the community banks. Then it proposes to improve the supervision system, strengthen technical support and talent reserve, and promote the development of online finance and credit reporting, in order to increase the core competitiveness of community banks in China.

Big Data Credit Report in Credit Risk Management of Consumer Finance

Traditional consumer finance is a modern financial service method that provides consumer loans to consumers of all classes. With the gradual improvement of China’s credit reporting system, big data credit reporting has effectively made up for the lack of traditional credit reporting and has been widely used in the consumer finance industry. In this context, the in-depth analysis of the specific application of big data credit reporting in the credit risk management of consumer finance and the strengthening of the research on the application of big data credit reporting in the credit risk management of consumer finance are urgently needed to be resolved in the economic and financial theoretical and practical circles’ problem. This article mainly studies the research on credit risk management of consumer finance by big data. The experimental results of this paper show that the model has a good forecasting ability, can distinguish between normal loan customers and default loan customers, and is suitable for practical personal credit risk control business. The prediction accuracy of the default model of the fusion model is 97.14%, and the default rate corresponding to the actual business is 2.86%. By combining the risk items such as the blacklist and gray list in the Internet finance industry, the bad debt rate and illegal usury can be well controlled to meet industry supervision.

Precise clustering analysis of Internet financial credit reporting dependent on multidimensional attribute sparse large data

Given that the traditional methods cannot perform clustering analysis on the Internet financial credit reporting directly and effectively, a kind of precise clustering analysis of internet financial credit reporting dependent on multidimensional attribute sparse large data is proposed. By measuring the overall distance between Internet financial credit reporting through the sparse large data with multidimensional attributes, the multidimensional attribute sparse large data are used to perform clustering analysis on the overall distance matrix and the component approximate distance matrix between the data, respectively. The correlation relationship between the Internet financial credit reporting under these two perspectives is taken into comprehensive consideration. Multidimensional attribute sparse large data pairs are used to reflect the comprehensive relationship matrix of the original Internet financial credit reporting to achieve clustering with relatively high quality. Numerical experiments show that compared with the traditional clustering methods, the method proposed in this paper can not only reflect the overall data features effectively, but also improve the clustering effect of the original Internet financial credit reporting data through the analysis of the correlation relationship between the important component attribute sequences.

An Analysis of the Current State of the Consumer Credit Reporting System in China

The Chinese Social Credit System (SCS), known as the first national digitally-implemented credit rating system, consists of two parallel arms: a government-run and a commercial one. The government-run arm of the SCS, especially efforts to blacklist and redlist individuals and organizations, has attracted significant attention worldwide. In contrast, the commercial part has been less often in the public spotlight except for discussions about Zhima Credit.The commercial arm of the SCS, also referred to as the Consumer Credit Reporting System (CCRS), has been under development for about two decades and took a major step forward in 2015 when 8 companies were granted permission to implement pilot consumer credit reporting programs. This development fundamentally increased the reach and impact of the SCS due to these companies’ sizable customer base and access to vast troves of consumer-related information.In this paper, we first map the Chinese CCRS to understand the actors in the credit reporting ecosystem. Then, we study 13 consumer credit reporting companies to examine how they collect and use personal information. Based on the findings, we discuss the relationship between the CCRS and the SCS including the changes in the power relationships between the government, consumer credit reporting companies and Chinese citizens.

The Tougher Fights

As the Consumer Financial Protection Bureau put down deeper roots, it took on tough issues. It worked with the Justice Department to eradicate discrimination in auto lending, which produced several enforcement actions but not a market-wide solution. It used its supervisory oversight to insist that the credit reporting companies improve the accuracy of their credit files and create reliable processes to correct errors. And it cleaned up abusive debt collection practices through major enforcement actions, by creating new tools to help consumers protect themselves and assert their rights, and by embarking on new rules to protect consumers while clarifying inconsistent and conflicting court rulings under the Fair Debt Collection Practices Act. The chapter also describes the bureau’s work with its partners to address the sprawling scandal at Wells Fargo, where thousands of bank employees misused customers’ data and money to open millions of phony bank and credit card accounts.