How signaling and search costs affect information asymmetry in P2P lending:the economics of big data

In the past decade,online Peer-to-Peer(P2P)lending platforms have transformed the lending industry,which has been historically dominated by commercial banks.Information technology breakthroughs such as big data-based financial technologies(Fintech)have been identified as important disruptive driving forces for this paradigm shift.In this paper,we take an information economics perspective to investigate how big data affects the transformation of the lending industry.By identifying how signaling and search costs are reduced by big data analytics for credit risk management of P2P lending,we discuss how information asymmetry is reduced in the big data era.Rooted in the lending business,we propose a theory on the economics of big data and outline a number of research opportunities and challenging issues.

Modern Electronic Information Industry to Promote Economic and Social Information—An Interview with Electronics Industry Minister Hu Qili

Spurred by the world information tide, China has organized a series of information projects, called the "Three Gold" projects. Recently I had an interview with Mr. Hu Qili, Minister of Electronics Industry, about the establishment of China’s modern Electronic Information Industry. Mr. Hu told me that information is the mark of development of a modern society and electronics is the major means of carrying information. Establishing

Economic Information Industry in China

Human history has experienced agricultural society and industrial society and is now entering the information society. The major economic developed countries of the world have drawn up and implemented their information plans. An information revolution is being carried out around the world. How to set up China’s information industry and economic information network is of

Information Segmentation and Investing in Cybersecurity

This paper provides an analysis of how the benefits of information segmentation can assist an organization to derive the appropriate amount to invest in cybersecurity from a cost-benefit perspective. An analytical model based on the framework of the Gordon-Loeb Model ([1]) is presented that provides a set of sufficient conditions for information segmentation to lower the total investments in cybersecurity and the expected loss from cybersecurity breaches. A numerical example illustrating the insights gained from the model is also presented.

The Economics of Sharing Unclassified Cyber Threat Intelligence by Government Agencies and Departments

This paper extends the literature on the economics of sharing cybersecurity information by and among profit-seeking firms by modeling the case where a government agency or department publicly shares unclassified cyber threat information with all organizations. In prior cybersecurity information sharing models a common element was reciprocity—i.e., firms receiving shared information are also asked to share their private cybersecurity information with all other firms (via an information sharing arrangement). In contrast, sharing of unclassified cyber threat intelligence (CTI) by a government agency or department is not based on reciprocal sharing by the recipient organizations. After considering the government’s cost of preparing and disseminating CTI, as well as the benefits to the recipients of the CTI, we provide sufficient conditions for sharing of CTI to result in an increase in social welfare. Under a broad set of general conditions, sharing of CTI will increase social welfare gross of the costs to the government agency or department sharing the information. Thus, if the entity can keep the sharing costs low, sharing cybersecurity information will result in an increase in net social welfare.

Probe on Network-based Collaborative Maintenance Model for After-sales Equipment

In order to make equipment run safely, economically and continuously, some new maintenance models were put forward to improve the equipment after-sale maintenance service, such as E-maintenance, third-party maintenance, etc. To certain extent, the models solved the problem of the distance between the manufacturer and customer and the dispersion of the maintenance technologies, however, those resources are still widely distributed and do not collaborate efficiently. In this paper, a network-based collaborative maintenance service model was proposed for after-sales equipment to solve the problem of maintenance resources integration. Concretely, equipment designers, maintainers, spare parts suppliers and maintenance experts were grouped together to establish dynamic alliance. The leader of the alliance is the manufacturer under guaranty period or equipment user exceeding the guaranty period. The process of maintenance service was divided into three stages which are fault diagnosis, maintenance decision and maintenance implementation. The sub-alliances were established to carry out maintenance work at each stage. In addition, the business process of network-based collaborative maintenance was analyzed and collaborative business system for equipment＇s after-sales collaborative maintenance service was designed. In the end, an informational economics model of network-based collaborative maintenance was established to demonstrate the effectiveness of this maintenance model.

Value of big data to finance:observations on an internet credit Service Company in China

Background:his paper presents a case study on 100Credit,an Internet credit service provider in China.100Credit began as an IT company specializing in e-commerce recommendation before getting into the credit rating business.The company makes use of Big Data on multiple aspects of individuals’online activities to infer their potential credit risk.Methods:Based on 100Credit’s business practices,this paper summarizes four aspects related to the value of Big Data in Internet credit services.Results:1)value from large data volume that provides access to more borrowers;2)value from prediction correctness in reducing lenders’operational cost;3)value from the variety of services catering to different needs of lenders;and 4)value from information protection to sustain credit service businesses.Conclusion:The paper also discusses the opportunities and challenges of Big Databased credit risk analysis,which needs to be improved in future research and practice.

Investing in Cybersecurity: Insights from the Gordon-Loeb Model

Given the importance of cybersecurity to the survival of an organization, a fundamental economics-based question that must be addressed by all organizations is: How much should be invested in cybersecurity related activities? Gordon and Loeb [1] presented a model to address this question, and that model has received a significant amount of attention in the academic and practitioner literature. The primary objective of this paper is to discuss the Gordon-Loeb Model with a focus on gaining insights for the model’s use in a practical setting.

Empirical Investigation of Threats to Loyalty Programs by Using Models Inspired by the Gordon-Loeb Formulation of Security Investment

Loyalty program (LP) is a popular marketing activity of enterprises. As a result of firms’ effort to increase customers’ loyalty, point exchange or redemption services are now available worldwide. These services attract not only customers but also attackers. In pioneering research, which first focused on this LP security problem, an empirical analysis based on Japanese data is shown to see the effects of LP-point liquidity on damages caused by security incidents. We revisit the empirical models in which the choice of variables is inspired by the Gordon-Loeb formulation of security investment: damage, investment, vulnerability, and threat. The liquidity of LP points corresponds to the threat in the formulation and plays an important role in the empirical study because it particularly captures the feature of LP networks. However, the actual proxy used in the former study is artificial. In this paper, we reconsider the liquidity definition based on a further observation of LP security incidents. By using newly defined proxies corresponding to the threat as well as other refined proxies, we test hypotheses to derive more implications that help LP operators to manage partnerships;the implications are consistent with recent changes in the LP network. Thus we can see the impacts of security investment models include a wider range of empirical studies.