Identification of Investment-Ready SMEs: A Machine Learning Framework to Enhance Equity Access and Economic Growth

Small and medium-sized enterprises (SMEs) are critical contributors to economic growth, innovation and employment. However, they often struggle in securing external financing. This financial gap mainly arises from perceived risks and information asymmetries creating barriers between SMEs and potential investors. To address this issue, our study proposes a machine learning (ML) framework for predicting the investment readiness (IR) of SMEs. All the models involved in this study are trained using data from the survey provided by the European Central Bank's Survey on Access to Finance of Enterprises (SAFE). In the empirical section of the paper, we train, evaluate and compare the predictive performance of nine (9) machine learning algorithms: Gradient Boosting, Random Forest, Logistic Regression, Support Vector Machines, Naïve Bayes and various ensemble methods. The results highlight the efficiency of ML algorithms in identifying investment-ready SMEs. In particular, the Gradient Boosting algorithm achieves a balanced accuracy of 75.4% and the highest ROC-AUC score at 0.815. Overall, this study contributes valuable insights to both academics and market participants by demonstrating how machine learning can help in bridging the SME financing gap. It can also offer clear inference to policymakers to design targeted interventions and can provide investors with efficient, data-driven methods for identifying promising SMEs. Ultimately, this research supports the broader goal of enhancing SME access to capital, fostering economic growth through informed policies and decisions, and enhancing the allocation of strategic economic resources.