Articlediamond2020

Corporate Default Forecasting with Machine Learning

Mirko Moscatelli · Simone Narizzano · Fabio Parlapiano · Gianluca Viggiano

25 citations0/0 files storedRisk Management Magazine

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Financial Distress and Bankruptcy Prediction Forecasting Techniques and Applications Credit Risk and Financial Regulations

Abstract

We compare statistical models usually employed in credit risk forecasting with machine learning algorithms (ML). Using a large dataset which includes financial ratios and credit behavioral indicators for about 300,000 Italian non-financial firms from 2011 to 2017, we show that training the models on financial statement data only, ML models record a significant improvement in discriminatory power and precision with respect to statistical models; however, this improvement is less pronounced when we enlarge the training dataset to include also credit behavioral data

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References

1968FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY 2017Machine learning models and bankruptcy prediction 2003Benchmarking state-of-the-art classification algorithms for credit scoring 2016Financial Distress Prediction in an International Context: A Review and Empirical Analysis of Altman's Z‐ Score Model 1983Corporate financial distress : a complete guide to predicting, avoiding, and dealing with bankruptcy 2010The Performance of Credit Rating Systems in the Assessment of Collateral Used in Eurosystem Monetary Policy Operations 2019Predicting Consumer Default: A Deep Learning Approach

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2021Data Science for Economics and Finance 2024Credit Risk Prediction Using Explainable AI 2024A Multi-Head LSTM Architecture for Bankruptcy Prediction with Time Series Accounting Data 2023Loan Default Prediction Based on Machine Learning Methods 2022Machine Learning for Zombie Hunting: Predicting Distress from Firms' Accounts and Missing Values

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