Toward a New Cloud-Based Approach to preserve the Privacy for Detecting Suspicious Cases of Money Laundering
Subtitle: ICCS 2014
Publisher: Association of Scientists, Developers and Faculties
Published: 2014
Place of publication: Bangkok, Thailand
Language: eng
Format: Hardcopy
Number of pages: 309
ISBN-13: 9788192523354
Description: Today, money laundering poses a serious threat not only to financial institutions but also to the nations. This criminal activity is becoming more and more sophisticated and seems to have moved from the cliché of drug trafficking to financing terrorism and surely not forgetting personal gain. Most international financial institutions have been implementing anti-money laundering solutions to fight investment fraud. On the other hand, cloud-based applications are merging daily and bringing to clients with lower cost of platforms and data storage, greater scalability and improved business continuity. Hence, more financial institutions aim to move their IT infrastructure to the cloud. However, accessing directly to the customer transaction datasets by a third party could be a confidential issue. This approach is more severe when these solutions are built by collaborating partners. Traditional methods are based on data access agreement but there is still a risk of infringing privacy. In order to preserve the privacy of datasets, different data disguising methods have been proposed. Nevertheless, analyzing disguised datasets is a performance issue in the context of detecting suspicious money laundering cases where the real value of data has an important impact. Indeed, the results of analysis could also be a privacy issue. Within the scope of a collaboration project for developing a new cloud-based solution for the Anti-Money Laundering Units in an international investment bank, in this paper, we propose new cloud-based approach using data disguising methods applied in analysing transaction datasets. We also show that the creating relevant dimensions from the current ones are efficient for analysing transaction datasets in terms of both detecting suspicious case and privacy preserving.