Preliminary Program: June 29th (all times EST)
Shining a light on the shadows: Endogenous trade networks and the growth of an online illegal market.
Although markets rely on governmental oversight to enforce contracts and guarantee cooperation in exchange, little research has considered how illegal markets, which evade and work against the state, grow and develop. Using unique transaction-level data on 7,196 market actors and 16,847 illegal drug exchanges on a “darknet” drug market, this study evaluates the network processes that shape online illegal drug trade and promote the growth of online illegal drug markets. While past research on online markets has found that networks have little influence on trade relationships due to market actors’ relative anonymity and because repeated transactions are rare, we argue that the high-risk context of illegal trade enhances market actors’ reliance on social relationships that emerge endogenously from transaction networks. Consistent with this explanation, findings reveal a highly structured trade network characterized by dense clustering and frequent recurrent drug exchange. Dynamic network models reveal that embeddedness and closure in exchange structure both increase the hazard rate of illegal drug trade with effect sizes comparable to formal reputations. These effects are pronounced in the early stages of market development, where risk is greatest. However, they become negative or insignificant once the market reaches maturity, where reputations are well-established. These findings demonstrate the powerful yet temporally contingent influence of transaction networks on illegal trade in online markets and reveal how endogenous networks of economic relations can promote risky exchange under conditions of relative anonymity and illegality.
Graph Convolutional Networks for Anti-Money Laundering
While deep learning has done remarkable things on Euclidean data (e.g. audio, images, video) graph deep learning has lagged because combinatorial complexity and nonlinearity issues making training very difficult and expensive. Yet it’s precisely the information hidden in that complexity that makes graphs so interesting. In this talk, Mark Weber will introduce a class of methods known as scalable graph convolutional networks (GCN) and share published experimental results from a semi-supervised anomaly detection task in financial forensics and anti-money laundering in Bitcoin.
Corporate networks in public procurement corruption scandals
Governments lose significant funds due to corruption in public procurement. While most efforts have focused on strategies to tackle fraud schemes at all stages of the contracting process, there are fewer analyses related to the corporate features of companies involved in procurement that would be most likely to signal organized criminal activities. In this talk, we explore four important and recent documented cases of local and federal corruption in public procurement in Mexico, where hundreds of regular and irregular companies were used to embezzle billions of dollars. In terms of criminal conspiracy, we discuss how the network analysis of companies at the ownership and management level reveals important collective behaviors that need to be considered in the design of procurement risk assessment frameworks.
Networks of tax evasion in Mexico. Martin Zumaya, Martha Gómez, Nephtalí Garrido, Carlos Gershenson, Gerardo Iñiguez, Carlos Pineda
Paths between economic and violent crimes: a network perspective. Georg Heiler, Jan Korbel, Tuan Pham, Stefan Thurner
Invitation to contribute
EPJ Data Science Special Issue
Contributors and attendees may be interested in the call for contributions for an upcoming issue of EPJ Data Science on the same topic as the satellite.
https://epjdatascience.springeropen.com/call-for-papers--data-science-perspectives-on-economic-crime
Organizers