Authors:
Hennie Daniels
1
and
Emiel Caron
2
Affiliations:
1
Center for Economic Research, Tilburg University; Erasmus Research Institute of Management (ERIM), Erasmus University, Netherlands
;
2
Erasmus Research Institute of Management (ERIM), Erasmus University, Netherlands
Keyword(s):
Decision Support Systems, Finance, Production Statistics, Artificial Intelligence, Explanation.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Biomedical Engineering
;
Business Analytics
;
Data Engineering
;
Data Mining
;
Databases and Information Systems Integration
;
Datamining
;
Enterprise Information Systems
;
Health Information Systems
;
Industrial Applications of Artificial Intelligence
;
Sensor Networks
;
Signal Processing
;
Soft Computing
Abstract:
In this paper, we describe an extension of the methodology for explanation generation in financial
knowledge-based systems. This offers the possibility to automatically generate diagnostics to support business decision tasks. The central goal is the identification of specific knowledge structures and reasoning methods required to construct computerized explanations from financial data and models. A multi-step look-ahead algorithm is proposed that deals with so-called cancelling-out effects. The extended methodology was tested on a case-study conducted for Statistics Netherlands involving the comparison of financial figures of firms in the Dutch retail branch. The analysis is performed with a diagnostic software application which implements our theory of explanation. Comparison of results of the method described in (Daniels and Feelders, 2001) with the results of the extended method clearly improves the analyses when cancelling-out effects are present in the data.