Catchem: A Browser Plugin for the Panama Papers Using Approximate String Matching

Abstract

The Panama Papers is a collection of 11.5 million leaked records that contain information for more than 214,488 offshore entities. This collection is growing rapidly as more leaked records become available online. In this paper, we present a work in progress on a web browser plugin that detects company names from the Panama Papers and alerts the user by means of unobtrusive visual cues. We matched a random sample of company names from the Public Works and Government Services Canada registry against the Panama Papers using three different string matching techniques. Monge-Elkan is found to provide the best matching results but at increased computational cost. Levenshtein-based approach is found to provide the best tradeoff between matching and computational cost, while Jacquard index like approach is found to be less sensitive to slight textual change.