Paper Info
Title
A Business Intelligence Model to Predict Bankruptcy using Financial Domain Ontology with Association Rule Mining Algorithm
Abstract
Today in every organization financial analysis provides the basis for understanding and evaluating the results of business operations and delivering how well a business is doing. This means that the organizations can control the operational activities primarily related to corporate finance. One way that doing this is by analysis of bankruptcy prediction. This paper develops an ontological model from financial information of an organization by analyzing the Semantics of the financial statement of a business. One of the best bankruptcy prediction models is Altman Z-score model. Altman Z-score method uses financial rations to predict bankruptcy. From the financial ontological model the relation between financial data is discovered by using data mining algorithm. By combining financial domain ontological model with association rule mining algorithm and Zscore model a new business intelligence model is developed to predict the bankruptcy.
Year
Venue
Keywords
2011
CoRR
business intelligence,association rule mining,corporate finance,financial analysis
Field
DocType
Volume
Financial modeling,Financial ratio,Data science,Data mining,Financial statement,Computer science,Accounting management,Algorithm,Financial analysis,Bankruptcy prediction,Business model,Finance,Business rule
Journal
abs/1109.1087
ISSN
Citations 
PageRank 
IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 3, No. 2, May 2011 ISSN (Online): 1694-0814
4
0.48
References 
Authors
4
3
Name
Order
Citations
PageRank
A. Martin1112.32
M. Manjula250.84
V. Prasanna Venkatesan3409.89