The Scamseek Project – Text Mining for Financial Scams on the Internet

Abstract

The Scamseek project, as commissioned by ASIC has the principal objective of building an industrially viable system that retrieves potential scam candidate documents from the Internet and classifies them as to their potential risk of containing an illegal investment proposal or advice. The project produced multiple classifiers for different types of data, and achieved higher than expected performance statistics on classifications. The development of the system required the solution of two major problems in document classification, namely accurate identification of classes with very small footprints, <.1%, and classification using meaning intention rather than word strings. The approach taken used Systemic Functional Grammar to model the semantics of the scam classes and used unigrams with significant language pre-processing to assist in separating irrelevant documents. Litigations have been initiated by ASIC from classifications made by the system. ASIC operates the system on a 24/7 basis. The estimate of savings in human effort in its monitoring role is the order of 100-fold. The estimate in savings to the community cannot be estimated readily but is likely to be of the order of tens of millions of dollars.