Do Lenders Make Optimal Decisions in a Peer-to-Peer Network?

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  • The activity of lending in a P2P Lending network differs from lending at traditional banking institutions in several important ways. It follows the principles of Grameen banking, where the goal is to give non-bankable borrowers access to a line of credit . The need for collateral is removed and the system is based on mutual trust. Unlike banks, in P2P lending Lenders can judge more than “just the numbers.” In addition, P2P lenders may have motivations beyond a return on investment, for example doing social good or helping a community friend. Nevertheless, the downside is that individual lenders lack the complex risk assessment models that banks utilize for their operations. Prosper Marketplace, Zopa, and Comunitae are examples of online platforms for Peer-To-Peer lending. Borrowers create personal profiles and solicit loans via online listings detailing the amount requested, maximum acceptable interest rate, and purpose for the loan. In turn, Lenders assess and can bid on listings; if the total dollar amount of bids is equal to the amount requested, the loan is materialized and the funding credited. Optionally, when total bid amount exceeds the amount requested, those Lenders electing the lowest interest rates are granted a stake in the loan. If a listing fails to garner complete funding, it is canceled by the system and the Borrower has the option to repost. P2P Lending sites collect a percentage of every fully-funded loan as it is repaid to the Lenders.
  • P2P LENDING ELEMENTS A Member is a registered user of the P2P Lending site. Members may have one or multiple roles that determine which actions the Member is allowed to perform on the site. A collection of Members who share a common interest or affiliation join into a Group. A Group is a community of Members that have a common interest. The Group is managed by a leader, who is in charge of admitting/rejecting applicants, and distributing rewards among Group Members. Each Group is rated by Prosper according to the loan performance of its Members. In order to avoid misunderstanding, it is necessary to clarify that the reputation of a Group is assessed by the P2P Lending site based merely on marketplace activity and not on the credit profiles of the members that are part of it. Therefore, members with poor Credit Scores can also be part of communities with the highest reputation in the marketplace, as long as they are accepted. Hence, it is wrong to assume that Groups with high reputation are formed by members with good credit profiles. Once admitted, individual members help positively or negatively the Group as with their performance. Groups are not rated until they have a representative history of activity, and after that they are rated on a one to five scale. A Bid is created when a Lender wishes to lend money to a Borrower in response to a Listing the Borrower created to solicit Bids. Bids are created by specifying an amount and a minimum rate the lender wishes to receive. In order to become a Loan, the Bids need to win the auction. Borrowers create Listings to solicit bids by describing themselves and the reason they are looking to borrow money. If the Listing receives enough bids by Lenders to reach the amount requested before the Listing period ends, it will become a loan. SOCIAL INFLUENCE AND SELECTION This study is focused on understanding social interactions in P2P Lending transactions. Like other online networking engines, P2P Lending marketplaces follow the fundamental properties of social networks, and behave according to two phenomena: social influence and selection . First, it is understood by social influence when a person diffuses its ideas to another person through interaction. Second, selection phenomenon is a reason why people tend to form communities and have relationships with persons who are similar to them. These two phenomenoa are present in peer-to-peer lending through the following common components of:     “ Friend” : It represents a one-to-one link from a member to other borrowers or lenders. This relationship between members is usually based on family, friendship or previous transaction history in the P2P Lending context. It is made public and intends to motivate lenders within borrower’s second degree social network to bid based on indirect trust. For example in the figure, Person A posted a listing and gets bids from its friend Person B. By social influence , Person C, who is friend of Person B, but not necessarily friend of Person A, is going to bid on Person A’s listing as well. “ Group” : Members are allowed to form communities. Group members help each other and the Group Rating depends on their performance. Groups are formed by the selection phenomenon, where individuals tend to trust those that share similarities with them. The trust among group members not only facilitates creating successful listings, but also generates peer pressure on colleague borrowers to force them to have an appropriate loan performance. Groups are managed by group Leaders who bring borrowers to the P2P Lending site, maintain the presence of the group in the site, and collect or share group rewards. Borrowers who are members of a group often get better interest rates because Lenders tend to have more confidence in Borrowers that belong to trusted groups. “ Endorsement” : Members are allowed to give public feedback on previous transactions with other members. This public feedback might alter the impression of friends or group colleagues through social influence .
  • Although P2P lending can be described as an online social network, its layer of economic transactions makes it unique: Trust and Profile verification: the identities and credit information of would-be members are verified by third party credit companies such as Experian. Typical social networks have no verification: research suggests that 8% of Facebook profiles use a fake name. Trust and credibility are important to support financial risk-taking; the public nature of verification distinguishes P2P lending from the customer-bank relationship, where data is kept private. Reputation: members’ history is critical to their success in the network. Borrowers aim to provide an attractive offer and have a good performance, so that they can get better conditions on a future credit request or be admitted a highly rated community in order to enjoy its benefits. The key difference between the reputation model of economic and professional social networks is that P2P lending enforces reputation based on objective facts (such as loan return performance), whereas LinkedIn, for example, is based on personal recommendations, which cannot be objectively weighted. Financial Transaction Marketplace: Online marketplaces such as eBay and Craigslist aim to provide a service in which users can exchange products. These sites do not provide social network capabilities and therefore transactions occur between complete strangers. Overstock.com combines marketplace and the social network concepts. The impact of the social network on Overstock’s performance has been studied . P2P lending sites share the economic and social components of Overstock; they differ in that no product is exchanged. Implicit links: The direct relationships are explicitly represented in the P2P network. However, there also exist “implicit links” that are never formalized as “Friends” or “Group” memberships. Every time a loan is converted, those Lenders who provided winning bids establish a person-to-person contract with the Borrower. This contract is temporal and its context is known by both parties. While this link is not formalized as a “Friend”, it does represent an implicit tie between members of the social network. The financial dynamic creates rules of interaction and social contracts that are not present in other social networks. Two-Sided Market: While most online communities have revenue streams from advertisement, P2P lending networks establish a two-sided market strategy where both sides are charged. Both Borrowers and Lenders pay for the service provided by the P2P auction site. Borrowers pay a 2-3% of closing fee and failed/late payment fees, and Lenders pay a 1% annual loan servicing fee.
  • Although a small percentage of lenders lend exclusively to borrowers with a single credit score, there is generally a large amount of variance in the type of borrowers that lenders seek to fund. An analysis of a sample of lenders shows the patterns of lending across credit scores on the right figure. While most lenders prefer, on the whole, to invest in borrowers with higher credit scores, several lenders analyzed actually show a preference for loans submitted by D or High Risk borrowers. On the aggregate, however, lenders strongly prefer borrowers with AA, A, B, or C credit grades (Right figure). Preferences largely mirror those of institutional lenders. Interestingly, investment preference does not decline significantly for borrowers with a C grade; at traditional banking institutions, C is considered a worrisome score. The left figure illustrates aggregate bids on borrowers in each credit score bin. Each ribbon represents an individual lender
  • So-called “social” features include the non-quantifiable elements of a borrower’s profile that might be used by a lender in decision-making. The figure illustrates the distribution of bids made by lenders by credit grade and group affiliation of the borrower. The pattern of lender preference with regard to credit grade does not vary across groups with different reputation. In general, lenders look for reasonably good credit profiles, although not necessary the best (AA), as a B or C score can mean a better interest rate. Low scores such as E or HR are often but not always neglected, and people with no credit usually are ignored. There is no significant variation of the trends with regard to the group rating of the lender.
  • The figure shows bid activity by credit score and affiliation of borrowers. For high group ratings, lending patterns with regard to credit score is more uniformly distributed. The distribution shows a very timid peak for credit scores of C and D. The percentages of bids for the best credit scores are not as large as in the analysis above, and low credit scores receive much more attention (almost double) when group performance is high. There are two details worth to be mentioned: 1) borrowers that are affiliated with groups that have not been rated do not seem to be trustworthy since Lenders look for the best AA profiles on this population. 2) Borrowers with poor scores receive more bids as the affiliation of their groups improves.
  • There are almost 53,000 Lenders that have at least one winning bid in the marketplace, and the total amount of money borrowed from them reached 310 million USD by December 2008. The right figure illustrates the distribution of Lenders depending on the amount of money they have bidden. This analysis only considers winning bids and not outbids, since winning bids reflect the real amount of money in the marketplace. It can be seen that in general, the number of lenders decreases as the bid amount increases. We speculate that the reason the ranges [$100 – $500] and [$1000 - $2500] stand out is due to the fact that these ranges might appeal new lenders as starting amounts to “try” the returns of investing in the marketplace.   The left figure highlights an interesting detail: 39.47% of the money in the marketplace is lent by only 1.70% of the population of lenders in the marketplace (> $50,000).
  • A large portion (91%) of listings never garner sufficient bids to convert to loans. We examine the behavior of would-be borrowers who repost a listing after it expires unfulfilled, and find that (of the minority that reposts rather than exiting the network), 70% re-list only once, and that fewer than 2% of borrowers lower the amount of their request. In a marketplace with near-perfect information, we would expect to see greater evidence of learning. Because of the large variance in lending behavior, changes in “subjective” features (the non-financial factors under the most immediate control of borrowers) would, on average, lead to eventual success in loan funding. Although further textual analysis of listing descriptions is needed, we see the above results as preliminary evidence that borrowers are either not learning (that is, not adjusting their interest rates and requested amounts to levels appropriate given their financial history), or that there is something endemic in the marketplace (a poor search function, for example) that prevents some borrowers from being matched to the lenders who would prefer their given profile.
  • The rational agent model predicts that individuals seek to maximize their expected utility in every transaction. Although the ideas of bounded rationality have undermined the theory that humans act as perfectly rational agents, the lending models of traditional banking institutions are human-less, optimizing for expected payoff. Therefore, it is apt to observe the extent to which economic dynamics in a peer-to-peer lending network deviate from the predictions of the expected utility model. The left figure shows the average interest rate by borrower credit grade, and the right figure shows the number of loans by credit grade bucket, in both cases separating paid loans and defaulted loans. The expected payoff of investment in a borrower with a credit score of AA is higher than that of an investment in a borrower with score E, as nearly half of borrowers with score E default. However, only %18,06 percent of total bids are made on loans from AA borrowers, signaling sub-optimal behavior by lenders. What can account for this non-rational behavior? Quite simply, lenders may be motivated by factors other than expected payoff. In addition, a lender may lack the wherewithal to calculate the expected utility of different investment options, or he may believe he is able to “do better than” expected utility by picking up on non-quantifiable features expressed in a loan listing description or photograph. While regression analysis indicates that the borrower’s credit grade is the most relevant feature to predict loan repayment, borrowers are also motivated by social features, and membership in a group can triple the probability that a loan is converted .   Borrower interest rates are not properly calibrated to reflect the higher risk of investing in loans with, for example, low borrower credit score. Rates for loans in the D, E and HR range of borrower score are approximately 1.5 times higher than for those in the AA, A, B, and C range; however, the expected payoff for the former group is much lower given the nearly 50% probability of default given a low credit grade. We suspect that with greater dispersion of information, interest rates would be better (if not fully) calibrated. At the same time, a suggested virtue of a peer-to-peer lending marketplace is the possibility of serving the “unbankable”. Further analysis is necessary to determine whether there exist lenders with an ability to “pick out” and invest in promising candidates with low credit scores. With full optimization, a peer-to-peer marketplace would be unable to serve the underbanked.
  • Our results suggest that, in a number of significant ways, lender behavior deviates from optimal decision-making in the face of risk. On the whole, lenders prefer to lend to borrowers with credit scores of C or higher, but there is a large amount of variance in choices. Additionally, borrowers rarely learn from their failed loans. The very goal of a peer-to-peer lending network, as initially envisioned, is to provide a platform for the weakest borrowers to establish credibility through good performance. Thus, variance in lending patterns is important if this class of borrower is to be supported. At the same time, the long-term viability of a network depends, to a certain extent, on the calibration of risk and reward; if lenders are systematically left short by weaker borrowers who default, the network will learn over time to avoid these lenders. That this learning is not instantaneous is, however, a testament to the complex interaction of quantifiable and very “human” features upon which decisions are based.
  • Do Lenders Make Optimal Decisions in a Peer-to-Peer Network?

    1. 1. Do Lenders Make Optimal Decisions in a Peer-to-Peer Network? K.Krumme, S.Herrero-Lopez
    2. 2. Agenda <ul><li>Introduction: Peer-To-Peer Lending </li></ul><ul><ul><li>Principles </li></ul></ul><ul><ul><li>P2P Lending Components </li></ul></ul><ul><ul><li>What makes P2P Lending unique? </li></ul></ul><ul><li>Decision patterns of lenders </li></ul><ul><ul><li>Variance in lender choice </li></ul></ul><ul><ul><li>Patterns by lender affiliation </li></ul></ul><ul><ul><li>Patterns by borrower affiliation </li></ul></ul><ul><ul><li>Distribution of lenders </li></ul></ul><ul><li>Borrowers don’t learn </li></ul><ul><li>Lenders fail to maximize expected payoff </li></ul><ul><li>Discussion </li></ul>
    3. 3. Introduction (I): Principles <ul><li>The application of Grameen Banking principles in social networks: </li></ul><ul><ul><li>Aims to give non-bankable borrowers access to a line of credit </li></ul></ul><ul><ul><li>Based on mutual trust, lenders can judge more than just the numbers. </li></ul></ul><ul><ul><li>Lender motivations beyond ROI, do social good, help community </li></ul></ul><ul><li>Emerging networks: Prosper (USA), Zopa (UK), Comunitae (Spain), Prestiamoci (Italy)… </li></ul><ul><li>Our goal: </li></ul>
    4. 4. Introduction (II): P2P Lending Components * <ul><li>‘ A’ is a member. </li></ul><ul><li>‘ Group of A’ accepts ‘A’ to be a member of the community </li></ul><ul><li>‘ A’ posts a credit request –listing- in the marketplace </li></ul><ul><li>‘ A’ is supported by the community receiving ‘bids’ from them. </li></ul><ul><li>The community support influences ‘B’ - ’A’s friend – and ‘C’ – ‘A’s 2 nd degree friend- to bid on ‘A’s listing </li></ul><ul><li>Prosper Marketplace opened its database for research to encourage better understanding of this emerging financial service. </li></ul><ul><li>This study is based on Prosper’s dataset and we follow their terminology </li></ul>
    5. 5. Introduction (III): What makes P2P Lending unique? <ul><li>Differences with other social networks </li></ul><ul><ul><li>Trust and Profile verification : Profiles are verified by third party credit companies. </li></ul></ul><ul><ul><li>Objective Reputation : P2P lending enforces reputation based on objective facts and not on personal recommendations. </li></ul></ul><ul><ul><li>Financial Transaction Marketplace : Combines marketplace and the social network concepts. </li></ul></ul><ul><ul><li>Implicit links : Winning bids establish a person-to-person contract with the Borrower. This contract is temporal and its context is known by both parties. </li></ul></ul><ul><ul><li>Two-Sided Market : Both Borrowers and Lenders pay for the service provided by the P2P auction site. </li></ul></ul>
    6. 6. Decision Patterns (I): <ul><li>Variance in lender choice: </li></ul><ul><li>Lenders strongly prefer borrowers with AA, A, B, or C credit grades. </li></ul><ul><li>Interestingly, investment preference does not decline significantly for borrowers with a C grade; at traditional banking institutions, C is considered a worrisome score </li></ul>Percentage of bids for borrowers in each credit score bin, by individual lender Aggregate bids on borrowers in each credit score bin. <ul><li>Each ribbon represents an individual lender. </li></ul>
    7. 7. Decision Patterns (II): <ul><li>Pattern by Lender affiliation: </li></ul><ul><ul><li>The pattern of lender preference with regard to credit grade does not vary across groups with different reputation. </li></ul></ul><ul><ul><li>In general, lenders look for reasonably good credit profiles, although not necessary the best (AA), as a B or C score can mean a better interest rate. </li></ul></ul><ul><ul><li>Low scores such as E or HR are often but not always neglected, and people with no credit usually are ignored. </li></ul></ul>Bids by Credit Grade and Lender Group Rating
    8. 8. Decision Patterns (III): <ul><li>Pattern by Borrower affiliation: </li></ul><ul><ul><li>For high group ratings, lending patterns with regard to credit score is more uniformly distributed. </li></ul></ul><ul><ul><li>The distribution shows a very timid peak for credit scores of C and D and 3 - 4 star groups. </li></ul></ul><ul><ul><li>Low credit scores receive much more attention (almost double) when group performance is high. </li></ul></ul>Bids by Credit Grade and Borrower Group Rating
    9. 9. Decision Patterns (IV): <ul><li>It can be seen that in general, the number of lenders decreases as the bid amount increases. We speculate that the reason the ranges [$100 – $500] and [$1000 - $2500] stand out is due to the fact that these ranges might appeal new lenders as starting amounts to “try” the returns of investing in the marketplace. </li></ul><ul><li>39.47% of the money in the marketplace is lent by only 1.70% of the population of lenders in the marketplace (> $50,000). </li></ul>Lender Distribution By Amount Bid Amount invested by Lender Bracket
    10. 10. Borrowers don’t learn <ul><li>Borrowers don’t learn! </li></ul><ul><ul><li>91% of the listings never garner sufficient bids to convert to loans. </li></ul></ul><ul><ul><li>70% of the borrowers re-list failed listings only once. </li></ul></ul><ul><ul><ul><li>Fewer than 2% of borrowers lower the amount of their request. </li></ul></ul></ul><ul><li>Near-perfect information marketplace: </li></ul><ul><ul><li>Should show evidence of learning in the form of changes in “subjective” features. </li></ul></ul><ul><li>We speculate: </li></ul><ul><ul><li>Borrowers do not adjust their interest rates and amounts to appropriate levels. </li></ul></ul><ul><ul><li>Something endemic in the marketplace. </li></ul></ul>
    11. 11. Lenders Fail to maximize expected payoff <ul><li>The lending models of traditional banking institutions are human-less, optimizing for expected payoff. </li></ul><ul><li>The expected payoff of investment in a lender with a credit score of AA is higher than that of an investment in a lender with score E, as nearly half of borrowers with score E default </li></ul><ul><li>Only %18,06 percent of total bids are made on loans with AA borrowers, signaling sub-optimal behavior by lenders. </li></ul><ul><li>Lenders may be motivated by: </li></ul><ul><ul><li>Factors other than expected payoff. </li></ul></ul><ul><ul><li>Lack the wherewithal to calculate the expected utility of different investment options, or he may believe he is able to “do better than” expected utility . </li></ul></ul>Average Interest Rate By Credit Grade # Loans By Credit Grade
    12. 12. Discussion <ul><li>Lender behavior deviates from optimal decision-making in the face of risk </li></ul><ul><ul><li>In general, Lenders prefer to lend to borrowers with credit scores of C or higher. </li></ul></ul><ul><ul><li>But, there is a large amount of variance in choices . </li></ul></ul><ul><li>Borrowers rarely learn from their failed loans </li></ul><ul><li>Conclusion: Long-term viability of a network depends on the calibration of risk and reward </li></ul><ul><ul><li>if lenders are systematically left short by weaker borrowers who default, the network will learn over time to avoid these lenders. </li></ul></ul><ul><ul><li>That this learning is not instantaneous is, however, a testament to the complex interaction of quantifiable and very “human” features upon which decisions are based </li></ul></ul>
    13. 13. Questions [email_address] , [email_address]

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