Articlehybrid2025

The possibilities of using AutoML in bankruptcy prediction: Case of Slovakia

12 citations0/0 files storedTechnological Forecasting and Social Change

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Financial Distress and Bankruptcy Prediction Machine Learning and Data Classification Imbalanced Data Classification Techniques

Abstract

Using machine learning (ML) and artificial intelligence to make predictions to increase efficiency will drive the upcoming fifth industrial revolution. This study investigates the application of automated machine learning (AutoML) in the prediction of company bankruptcies, with a focus on two key novelties: (1) a comprehensive comparison of five state-of-the-art AutoML tools (AutoGluon, AutoKeras, H2O-AutoML, MLJar, and TPOT) against traditional statistical methods and ensemble ML techniques based on predictive performance and development time, and (2) an in-depth impact analysis of three distinct data resampling approaches (without resampling, random oversampling and SMOTE) on model performance and development time. Using financial data from 2019 to 2021, this study demonstrates that AutoML tools, particularly H2O-AutoML and AutoGluon, outperform traditional and ensemble ML methods (achieving AUC values of 0.913 and 0.894 respectively, compared to 0.880 for XGBoost) and significantly reduce model-development time, often completing tasks in one-third to half the time required by conventional approaches. Furthermore, the findings highlight the robustness of H2O-AutoML and AutoGluon in handling imbalanced datasets- a critical challenge in bankruptcy prediction. Therefore, selected AutoML methods can already help to democratise access to advanced risk management models for smaller companies and institutions to leverage high-performing predictive tools with minimal expert intervention. • AutoML (e.g., H2O-AutoML) surpasses ensemble ML methods in bankruptcy prediction. • AutoML excels in model performance and reduces time for model development. • AutoML handles imbalanced data effectively and simplifies model development. • Democratises risk management by making predictions accessible to smaller entities

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 12 citations exist upstream.

References

1968FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY 1980Financial Ratios and the Probabilistic Prediction of Bankruptcy 1966Financial Ratios As Predictors of Failure 2017Machine learning models and bankruptcy prediction 2014Prediction of financial distress: An empirical study of listed Chinese companies using data mining 2016Ensemble boosted trees with synthetic features generation in application to bankruptcy prediction 2016Financial ratios and corporate governance indicators in bankruptcy prediction: A comprehensive study 2021CatBoost model and artificial intelligence techniques for corporate failure prediction

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.