Financial Distress Prediction for Small and Medium Enterprises Using Machine Learning Techniques


  • Aidas Malakauskas Swedbank AB, Lithuania
  • Aušrinė Lakštutienė Kaunas University of Technology, Lithuania



Financial distress prediction, Random Forest, Artificial Neural Networks, Logistic Regression, Small and Medium Enterprises


Financial distress prediction is a key challenge every financing provider faces when determining borrower creditworthiness. Inherent opaqueness of Small and Medium Enterprise business complicates credit decision making process, therefore increasing cost to finance and lowering probability of receiving funds. This paper used data on 12.000 SMEs to estimate binomial classifiers for financial distress prediction using Logistic Regression, Artificial Neural Networks and Random Forest techniques. Classical financial ratios were used to estimate initial single-period predictors, which were later enhanced with time, credit history and age factors to retrieve multi-period models. Contrary to other studies, financial distress is understood as a significant challenge to company’s ability to cover liabilities rather than probability to go bankrupt. Highest prediction accuracy was reached using Random Forest algorithm with additional factors. It was concluded that period-at-risk adjustment is necessary to ensure highest financial distress prediction accuracy.

Author Biographies

Aidas Malakauskas, Swedbank AB, Lithuania

Aidas Malakauskas is a candidate for Ph.D. in economics in Kaunas University of Technology and working in AB Swedbank as the Head of Financing Transformation Department. He is also a member of Sustainable Economics Research Group in School of Economics and Business at Kaunas University of Technology. Research interests are in the areas of credit risk, SMEs, access to finance, credit rationing, and machine learning.

Aušrinė Lakštutienė, Kaunas University of Technology, Lithuania

Ausrine Lakstutiene is a Ph.D. in economics and an associate professor at Kaunas University of Technology. She is also a committee member in the Finance study programme, member of the Sustainable Economics Research Group, School of Economics and Business, Kaunas University of Technology. Research interests are in the areas of risk management, insurance risk, financial services, financial services development, and business financing sources.

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