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

TL;DR

This paper introduces a tree-based method to automatically detect redundant business rules in large financial datasets, aiming to improve efficiency and accuracy in NAV validation processes.

Contribution

The paper presents a novel tree-based approach for identifying correlated and redundant business rules in large-scale financial data, enhancing automation in NAV validation.

Findings

Effective detection of redundant rules demonstrated on real-world data

Reduces manual effort in rule validation processes

Improves accuracy and efficiency of NAV validation

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.