Bankruptcy Prediction using Diophantine Neutrosophic Number for Enterprise Resource Planning on Value of Accounting Information

Adeeb Alhebri¹, Gubarah Farah Gubarah^2,*, Abdulkarim Alsayegh³, Radwan Hussien Alkebssi⁴, Ebrahim Mohammed Al-Matari⁵

¹Accounting Program, Applied College at Muhyle, King Khalid University, Saudi Arabia.

²Applied Management Program, Applied College at Tanumah, King Khalid University, Saudi Arabia.

³Ph.D. Holder, Ministry of Education, Kuwait.

⁴Accounting Department, Business School, Xi’an International studies university, China.

⁵Department of Accounting, College of Business, Jouf University, Saudi Arabia, and Faculty of Commerce and Economics, Amran University, Amran, Yemen.

Emails: aalhebri@kku.edu.sa; gmohamed@kku.edu.sa; dr.abdulkarim_alsayegh@hotmail.com; Ralkebsee@xisu.edu.cn; Ibrahim_matri7@yahoo.com

Abstract

Enterprise Resource Planning (ERP) is paramount in modern business, integrating many fundamental processes such as human resources, economics, customer relationship management, and supply chain management into a comprehensive infrastructure. Leveraging the wide-ranging data apprehended by ERP techniques, an organization could improve its financial analysis abilities, involving bankruptcy prediction. By using analytics methods like predictive modeling and machine learning, the ERP system could examine market trends, historical financial information, key performance indicators, and other related factors to evaluate the financial stability and health of the company. This prediction insight empowers businesses to vigorously detect advanced indicators of financial distress, alleviate risks, and make informed strategic decisions to avoid bankruptcy. Integrating bankruptcy prediction techniques within the ERP system allows organizations to reinforce contingency strategies, financial planning, and risk management, protecting long-term competitiveness and sustainability in a dynamic business environment. This study introduces a Bankruptcy Prediction using the Diophantine Neutrosophic Number for Enterprise Resource Planning (BPDNN-ERP) technique on the value of accounting information. The BPDNN-ERP technique begins with a harmony search algorithm (HSA) for electing feature subsets. In addition, the BPDNN-ERP technique applies the DNN model for the prediction of bankruptcies. To increase the performance of the DNN model, the manta ray foraging optimization (MRFO) model can be used. The experimental study demonstrated the enhanced performance of the BPDNN-ERP algorithm equated to existing forecasting methods

Keywords: Bankruptcy Prediction; Enterprise Resource Planning; Harmony Search Algorithm; MRFO; Diophantine Neutrosophic Number