Name
Abstract | ||
|---|---|---|
The Panama Papers represent a large set of relationships between people, companies, and organizations that had affairs with the Panamanian offshore law firm Mossack Fonseca, often due to money laundering. In this paper, we address for the first time the problem of searching the Panama Papers for people and companies that may be involved in illegal acts. We use a collection of international blacklists of sanctioned people and organizations as ground truth for bad entities. We propose a new ranking algorithm, named Suspiciousness Rank Back and Forth (SRBF), that leverages this ground truth to assign a degree of suspiciousness to each entity in the Panama Papers. We experimentally show that our algorithm achieves an AUROC of 0.85 and an Area Under the Recall Curve of 0.87 and outperforms existing techniques.
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Year | DOI | Venue |
|---|---|---|
2018 | 10.5555/3382225.3382393 | ASONAM '18: International Conference on Advances in Social Networks Analysis and Mining
Barcelona
Spain
August, 2018 |
Field | DocType | ISBN |
Panama,Ranking,Task analysis,Computer science,Computer security,Ground truth,Blacklisting,Artificial intelligence,Machine learning,Money laundering | Conference | 978-1-5386-6051-5 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mikel Joaristi | 1 | 0 | 1.69 |
| Edoardo Serra | 2 | 5 | 4.87 |
| Francesca Spezzano | 3 | 80 | 19.08 |