The Impact of Machine Learning on Predicting Corporate Failure in Financial Reporting

Abstract

Corporate failure is a critical phenomenon that poses a significant threat to economic stability and public trust in financial markets. With the rapid advancement of technology, machine learning has emerged as a promising tool for the early detection of potential corporate failures through financial reporting. This literature study explores the contributions of machine learning methods in enhancing the accuracy of corporate failure prediction based on historical financial data. The analysis focuses on the effectiveness of various machine learning algorithms, including their capabilities in identifying financial anomalies and hidden patterns that may indicate early warning signs. In addition, this study addresses the challenges faced in the implementation of machine learning across different financial contexts, such as data imbalance, algorithmic bias, and model interpretability. The findings of this review conclude that machine learning significantly improves the predictive power of financial reporting, provided that the data is well-managed and that modeling is conducted in accordance with ethical principles and transparency standards.