A Business Intelligence Model to Predict Bankruptcy using Financial Domain Ontology with Association Rule Mining Algorithm

TL;DR

This paper presents a novel business intelligence model that combines financial domain ontology, association rule mining, and the Altman Z-score to improve bankruptcy prediction accuracy.

Contribution

It introduces an integrated approach that leverages financial ontology and data mining to enhance bankruptcy prediction beyond traditional models.

Findings

Improved accuracy in bankruptcy prediction.

Effective integration of ontology and association rule mining.

Enhanced understanding of financial data relationships.

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.