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A Tree-based Approach for Detecting Redundant Business Rules in very Large Financial Datasets

Authors: 

NhienAn LeKhac, Sammer Markos, Tahar Kechadi

Publication Type: 
Refereed Original Article
Abstract: 
Net Asset Value (NAV) calculation and validation is the principle task of a fund administrator. If the NAV of a fund is calculated incorrectly then there is huge impact on the fund administrator; such as monetary compensation, reputational loss, or loss of business. In general, these companies use the same methodology to calculate the NAV of a fund, however the type of fund in question dictates the set of business rules used to validate this. Today, most Fund Administrators depend heavily on human resources due to the lack of an automated standardized solutions, however due to economic climate and the need for efficiency and costs reduction many banks are now looking for an automated solution with minimal human interaction; i.e., straight through processing (STP). Within the scope of a collaboration project that focuses on building an optimal solution for NAV validation, in this paper, we will present a new approach for detecting correlated business rules. We also show how we evaluate this approach using realworld financial data
Digital Object Identifer (DOI): 
10.4018/jbir.2012100101
Publication Status: 
Published
Date Accepted for Publication: 
Thursday, 13 April, 2017
Publication Date: 
15/05/2017
Journal: 
International Journal of Business Intelligence Research (IJBIR) 3(4
Institution: 
National University of Ireland, Dublin (UCD)
Open access repository: 
No