Automated financial diagnosis: a comparison with other diagnostic domains
Journal of Information Science
Building the data warehouse (2nd ed.)
Building the data warehouse (2nd ed.)
The data warehouse toolkit: practical techniques for building dimensional data warehouses
The data warehouse toolkit: practical techniques for building dimensional data warehouses
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Automated explanation of financial data
International Journal of Intelligent Systems in Accounting and Finance Management - Risk Analysis in Complex Systems: Intelligent Systems in Finance
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In this paper the authors describe a method to integrate explanatory business analytics in OLAP information systems. This method supports the discovery of exceptional values in OLAP data and the explanation of such values by giving their underlying causes. OLAP applications offer a support tool for business analysts and accountants in analyzing financial data because of the availability of different views and managerial reporting facilities. The purpose of the methods and algorithms presented here, is to extend OLAP applications with more powerful analysis and reporting functions. The authors describe how exceptional values at any level in the data, can be automatically detected by statistical models. Secondly, a generic model for diagnosis of atypical values is realized in the OLAP context. By applying it, a full explanation tree of causes at successive levels can be generated. If the tree is too large, the analyst can use appropriate filtering measures to prune the tree to a manageable size. This methodology has a wide range of applications such as interfirm comparison, analysis of sales data and the analysis of any other data that possess a multi-dimensional hierarchical structure. The method is demonstrated in a case study on financial data.