Exploring Trends and Methodologies in Financial Distress Prediction

A Review

Authors

  • Sana Ramzan University Canada West

Abstract

This full research paper provides a systematic review of trends and methodologies in financial distress prediction over the past decade (2013-2023). The presentation will examine the evolution of predictive models, analytical techniques, and key variables used in forecasting financial distress across various industries. The research highlights the increasing integration of machine learning, artificial intelligence, and non-financial indicators in contemporary prediction frameworks, offering valuable insights for financial analysts, risk managers, and academic researchers.

Author Biography

Sana Ramzan, University Canada West

Sana Ramzan is an accomplished accounting professional and researcher with extensive experience in teaching and research. She is a business analytics faculty at the University of Canada West. Sana holds a Master of Science in Accounting from the American University of Sharjah and is pursuing a Doctor of Business Administration at Royal Roads University, where her research focuses on the intersection of artificial intelligence and accounting. Her work investigates how AI and machine learning can enhance accounting conservatism, audit quality, and fraud detection by analyzing financial, economic and governance variables. Sana has published multiple research papers, including in the Journal of Accounting Literature and International Journal of Management and Applied Science. Her research presentations have garnered recognition, including best paper awards at international conferences. With a strong academic foundation and a passion for integrating technology into accounting practices, Sana aims to provide actionable insights to improve corporate financial management and auditing processes.

Published

2025-09-12

Issue

Vol. 2 No. 1 (2025): 2025 PDGIA / IDGI Conference Proceedings

Section

2025 Conference Proceedings

License

Copyright (c) 2025 Sana Ramzan

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.