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By identifying that signaling and search costs are reduced by the application of big data analytics in P2P lending, we show how big data can reduce information asymmetry in the lending industry

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

Financial Innovation, no. 19 (2015)

Cited by: 21|Views21
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Abstract

Abstract 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 paradig...More

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Introduction
  • The ability to access credit is a critical lifeline for Small- and Medium-sized Enterprises (SMEs), which represent a large proportion of the total economy around the world.
  • The authors take an information economics perspective to investigate how information signaling and search costs affect information asymmetry in the lending business and analyze how big data reduces information asymmetry in P2P lending.
  • Given different IT capabilities in collecting and evaluating a borrower’s credit information, online lending platforms adopt different strategies in their business models.
Highlights
  • The ability to access credit is a critical lifeline for Small- and Medium-sized Enterprises (SMEs), which represent a large proportion of the total economy around the world
  • P2P lending platforms are using a wide range of data to evaluate credit risk, while traditional banks may not have the technical ability or analytical skills to utilize these new forms of data
  • Big data-based financial innovations have been embraced as a disruptive force that will reshape the financial services sector
  • We take an information economics perspective to investigate how big data affects the transformation of the lending industry
  • By identifying that signaling and search costs are reduced by the application of big data analytics in P2P lending, we show how big data can reduce information asymmetry in the lending industry
  • This paper discusses theoretical guidelines for broadening and improving research on P2P lending with a perspective of big data economics, and we plan to conduct empirical studies in our future research to validate what we suggest in this paper
Results
  • The authors will discuss how big data analytics provides an opportunity for credit risk management in P2P lending.
  • Online P2P lending platforms serve as such an agent between lenders and borrowers to reduce both the search and signaling costs.
  • Third-party certified information Fig. 3 Big Data Analysis for Credit Risk Management in Ali Finance Platform technologies, make it possible to significantly reduce information asymmetry in P2P lending.
  • P2P lending platforms are using a wide range of data to evaluate credit risk, while traditional banks may not have the technical ability or analytical skills to utilize these new forms of data.
  • The fundamental change is that the philosophy of analyzing credit risk has transformed from passive information retrieval into proactive big data analytics.
  • In traditional credit evaluation, lenders passively depend on the borrowers providing information about themselves; while in the big data era, lenders can proactively search the 360-degree online footprint of potential borrowers and let data tell who they really are.
  • Model Rank is based upon an internally developed big data algorithm that analyzes the performance of borrower members and takes into account the FICO score, credit attributes, and other application data.
  • Big data analytics make it possible for P2P lending platforms to better evaluate borrowers and make quick lending decisions.
  • Big data analytics, based on business intelligence models with large amounts of data from multiple data sources, is applied in P2P lending to evaluate credit risk.
  • This application of big data analytics in P2P lending would greatly reduce signaling and search costs, reducing information asymmetry between borrowers and lenders.
  • Based on the discussion in this study, it is clear that introducing big data analytics to the lending business has caused tremendous business and economic impacts.
Conclusion
  • The authors take an information economics perspective to investigate how big data affects the transformation of the lending industry.
  • By identifying that signaling and search costs are reduced by the application of big data analytics in P2P lending, the authors show how big data can reduce information asymmetry in the lending industry.
  • It is hoped that this paper will be a starting point for more research and discussion on big data-based financial innovations
Study subjects and analysis
categories of determinants: 3
Most prior academic studies on risk management in online P2P lending have focused on how to utilize online information for credit risk management. Three categories of determinants of default risk are proposed: loan characteristics, borrower characteristics, and borrower’s group characteristics (Everett 2008). Since information asymmetry is the major barrier for the lender to reduce default risk, several studies focus on how to mitigate information asymmetry between borrowers and lenders in the lending process (Freedman et al 2011)

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