This work was develped with Prof. Paulo Caetano, PhD.
It has been accepted at IADIS ICWI’14 in Porto, Portugal.
Abstract:
The Information Technology has led many countries to adopt technologies based on internationally accepted standards for the disclosure of their financial statements. The XBRL technology is adopted to represent financial data (instances) and its semantics (which are based on XML Schema and XLink) to facilitate the exchange of data and increase the transparency of financial information available on the Internet. However, the occurrences of financial crime in large corporations require that computational solutions are designed to detect fraud. With such goal, this paper proposes a support tool for forensic financial analysis, based on OLAP query languages for the digital detection of fraud in financial reports represented in XBRL. Operators were incorporated at the Mondrian server, an OLAP tool, which supports queries in XML documents connected by XLink and XML Schema (the basis of XBRL) and also relational databases. The forensic operators are presented, extending those defined in LMDQL (Link-based and Multidimensional Query Language), and its use is demonstrated on a relational repository of multidimensional data (based on the XBRL specification). Since XBRL expresses the semantics of the financial data, whose relationships among accounting elements (described in XBRL Schema) are commonly defined in one or more linkbases, this repository contains tables that store the data referring to these relationships. To review the forensic operators a case study was realized using XBRL documents and linkbases provided by U.S. SEC, to which an ETL processing was applied for the data loading, making it possible to analyze the occurrence of fraud in large volumes of data presented.
This paper has been published by IADIS Library – Financial Forensic Analysis, ISBN: 978-989-8533-24-1.
More details: https://marcioalexandre.wordpress.com/projects/financial-olap/
Software Engineering 