The Effectiveness of Machine Learning Techniques in Corporate Bankruptcy Risk Prediction

Abstract

This article examines how effective machine learning techniques are in predicting corporate bankruptcy risk in a context where early warning signals are crucial for lenders, investors, and regulators to contain financial instability. The study conducts a systematic literature review of peer reviewed research published between 2020 and 2024 that applies machine learning models to corporate bankruptcy and financial distress prediction. The evidence shows that ensemble, deep learning, and sequential models generally outperform traditional statistical approaches, particularly when class imbalance is addressed and financial data are enriched with market, textual, or governance related features. The article discusses these findings through a narrative and thematic synthesis that compares algorithms, feature sets, sampling strategies, and validation designs across different countries and sectors. The main findings highlight that modelling choices and data design strongly condition performance, that interpretability and governance remain underdeveloped, and that future work should link predictive gains more clearly to tangible improvements in risk management outcomes.