Abstract:
This article explores modern methods used in assessing going concern within financial audits, highlighting the shift from traditional, indicator-based evaluations to advanced techniques powered by artificial intelligence and machine learning. Models such as neural networks, LSTM, GRU, and NLP tools enable auditors to detect financial risks early, analyze patterns in large datasets, and predict insolvency with high accuracy. Additionally, stress testing and hybrid models improve forecasting capabilities. While automation enhances audit quality, concerns remain about transparency, data bias, and the need for specialized training. The study emphasizes a balanced approach that combines technological tools with auditor judgment. CZU: 657.6:004.8; JEL: M42
Description:
LUNGU, Alexandrina. Auditul continuității activității: metode moderne de analiză și evaluare = Going Concern Audit: Modern Methods of Analysis and Evaluation. Online Cord. șt.: Elena PETREANU. In: Challenges of Accounting for Young Researchers: International Student Scientific Conference, ISSC 2025, 9th Edition, March 14-15, 2025: Collection of scientific articles. Chişinău: SEP ASEM, 2025, pp. 53-55. ISBN 978-9975-168-25-0 (PDF). Disponibil: https://doi.org/10.53486/issc2025.12