Many cybercriminal entrepreneurs lack the skills and techniques to provision certain parts of their business model, leading them to outsource these parts to specialized criminal vendors. Online anonymous markets, from Silk Road to AlphaBay, have been used to search for these products and contract with their criminal vendors. While one listing of a product generates high sales numbers, another identical listing fails to sell. In this paper, we investigate which factors determine the performance of cybercrime products.To answer this question, we analyze scraped data on the businessto-business cybercrime segments of AlphaBay (2015-2017), consisting of 7,543 listings from 1,339 vendors, sold at least 126,934 times. We construct new variables to capture product differentiators and price. We capture the influence of vendor characteristics by identifying five distinct vendor profiles based on latent profile analysis of six properties. We leverage these product and vendor characteristics to empirically predict the performance of cybercrime products, whilst controlling for the lifespan and type of solution. Consistent with earlier insights into carding forums, we identify prevalent product differentiators to be influencing the relative success of a product. While all these product differentiators do correlate significantly with product performance, their explanatory power is lower than that of vendor profiles. When outsourcing, the vendor seems to be of more importance to the buyers than product differentiators.

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Underground marketplaces have emerged as a common channel for criminals to offer their products and services. A portion of these product comprises the illegal trading of consumer products such as vouchers, coupons, and loyalty program accounts that are later used to commit business fraud. Despite its well-known existence, the impact of this type of business fraud has not been analyzed in depth before. By leveraging longitudinal data from 8 major underground markets from 2011-2017, we identify, classify and quantify different types of business fraud to then analyze the characteristics of the companies who suffered from them. Moreover, we investigate factors that influence the impact of business fraud on these companies. Our models show that cybercriminals prefer selling products of well-established companies, while smaller companies appear to suffer higher revenue losses. Stolen accounts are the most transacted items, while pirated software together with loyalty programs create the heaviest revenue losses. The estimated criminal revenues are relatively low, at under $600 000 in total for the whole period; but the total estimated revenue losses are up to $7.5 millions.

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