Title: A Tree-based Approach for Detecting Redundant Business Rules in very Large Financial Datasets,
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 real-world financial data.