For
a forensic implication of online auction market earnings, we study networked
overlapping online auctions and underlying agent strategy. Bidder type
identification provides efficient prior information for price formation
process. Especially early-stage recognition of specific bidder types enables
faster ex-ante revenue estimation. Characterizing behavioral pattern of bidding
strategies, we identify unique digital signatures of heterogeneous bidder
types. Given that the bidder types impose direct impact on the revenue, we
further extend the conceptual domain to the potential bidding fraud which
undermines overall revenue structure. We highlight the method of agent
signature identification through Benford’s law and power law. In our findings,
there exists a bidder class which confirms the distributional pattern of
Benford’s law and their revenue impact is significant. Explicit
characterization is conducted based on the power law. Participating agent
strategy reflects their agent cost, surplus as well as market earnings.
