Unmasking Auction Fraud: Detecting and Preventing Shill Bidding

1. Shillin’ like a Villian: Fraud in Online Auction Markets Han Yang Lee and Tom Xu ECON1465, Fall 2011 Brown University

3. Introduction • A shill typically refers to someone who purposely gives the impression that he or she is an enthusiastic independent customer of a seller that he or she is secretly working for. Competitive Behavior (1) A shill tends to bid exclusively in auctions only held by one particular seller. (2) A shill tends to have a high bid frequency. (3) A shill has few or no winnings for the auctions participated in. (4) It is advantageous for a shill to bid within a small time period after a legitimate bid.  (5) A shill usually bids the minimum amount required to outbid a legitimate bidder. (6) A shill's goal is to try and stimulate bidding.

4. Types of Shills Reserve Price Shilling Seller shills in order to avoid auction house fees. Competitive Shilling Seller shills to drive up the value of the final bid.  Premium Bid A bid that is higher than other bids for the same item in different auctions. Ebay Fee Structure

5. Existing Literature Barbaro, S. and Bracht, B. (2006).  “Shilling, Squeezing, Sniping: Explaining late      bidding in online second-price auctions,” working paper. Engelbrecht-Wiggans, R. (1987).  On Optimal Reservation Prices in Auctions.        Management Science 33 (6), 763-770. R. J. Kauffman and C. A. Wood. Running up the bid: detecting, predicting, and      preventing reserve price shilling in online auctions. In International      Conference on Electronic Commerce, Pittsburgh, PA, 2003 R.J. Kauffman and C.A. Wood, The effects of shilling on final bid prices in online     auctions, Electronic Commerce Research and Applications 4(1) Spring (2005)     21–34. Myerson, R.B. (1981). Optimal Auction Design. Mathematics of Operations      Research 6. 58-73. J. Trevathan and W. Read Detecting Shill Bidding in Online English Auctions.     Technical Report,  James Cook University, May 2006. Wang, Wenli, Zoltan Hidvegi, and Andrew Whinston. Shill-Proof Fee (SPF)      Schedule: the Sunscreen against Seller Self-Collusion in Online English      Auctions.

6. Explored Hypothesis through Empirical Data Final Price Exploration 1. Winner's Curse Hypothesis (NE) 2. Value Signal Hypothesis (E) 3. Rational Bidder Hypothesis (NE) Seller Behavior Exploration 1. Repeatable Shilling Behavior Hypothesis (E) 2. Experienced Seller Hypothesis (NE) 3. Reputation Hypothesis (NE)

7. Possible Solutions Shill-Proof Fee Strengths: -Alters fee structure -Instead of final fee based on reserve price + final price, it will be based on fixed listing price + (final price - reserve price) -Sellers become indifferent -Does not affect honest sellers/buyers Weakness: -Theoretical assumption of IPV -SPF commission rate must be calculated per auction Detection Algorithm Strengths: -Attempts to find a bidding pattern -Finds a "shill score" for each bidder     1. Bidder/Seller relationship     2. Aggressive bids     3. Bidder winning percentage     4. Bid speed     5. Incremental Bid     6. Earliness of Bid Weakness: -Sellers can change strategy, but will be less efficient

8. Proposed Experiment - Summary • Seller assigned item from v = [50, 500], uniform distribution (discrete whole values) • Recommend reserve price = .5v (UB) • Seller then sets reserve price • Auction time is 2 min. • First 30 seconds only seller can bid • Auction then continues • Final price determines payoff for seller • After each round seller assigned % based off of final price – reserve price fee, normalized to v • Minimal reserve price is 1 • Higher % per round = higher payout for participants • Three groups, ten participants per group, [10, 15] rounds • Two stages as each participant partakes in two groups • Fee structure similar to that of eBay

9. Proposed Experiment - Bidders Behavior • Each auction has 10 bidders, automated • When a bidder wants to bid, timing of bid [1, 10] seconds, uniformly distributed • Each bidder has a private value assigned each round • N(v, (.1v)2) – rounded to nearest whole value • Auction has minimum incremental bid of .01v

10. Proposed Experiment - Groups Detection Algorithm -Simplification of model from Trevathan and Read -Depending on the difference between recommend reserve and actual, we set a probability of detection -If caught, sellers are forbidden to participate in next round Shill Proof Fee -Adopt simplified SPF recommended in Wang et al.  -Since all variables set, commission fee will be fixed (sellers will not know) -To start the experiment, each seller will know the fee changes from the control group

11. Hypothesis of Experiment Results 1) Believe that the SPF will be more effective as it is a preventative measure 2) We hypothesize an early spike in shilling but then gradual decrease as seller realize that shilling has no effect 3) Cost of SPF potentially lower than detection algorithm 4) Changing incentives, may be more efficient than punishment 5) Potential for interesting results in Stage 1 vs. Stage 2

12. Conclusion • Concern of lack of incentive for eBay to prevent shilling • Evidence that large sellers often shill • Light punishment for offenders • Shilling might generate large revenues • Experimental results can potentially give insight • Seller behavior: incentive changes vs. punishment • Further Research • Empirical cost data • Smaller auction houses • Competitive bidding prevention • Buyer behavior - sniping