Enhancing Insolvency Prediction Accuracy: a Random Forest-based Algorithmic Approach

Frame of the research. Insolvency prediction stands as a pivotal concern for managers, investors, and regulatory bodies alike, given its implications for the sustainability and stability of firms. The capability to furnish precise forecasts not only facilitates proactive decision-making but also safeguards the interests of stakeholders. This study undertakes an exploration into the transformative potential of the Random Forest algorithm in reshaping the landscape of insolvency prediction, offering a scholarly contribution that transcends the boundaries of conventional methodologies. Purpose of the paper. The aim of this paper is to conduct an in-depth investigation into the efficacy of employing Random Forest algorithm to enhance the accuracy of insolvency prediction models, with a specific focus on Italian firms. This work aspires to not only mitigate the shortcomings prevalent in traditional methods but also to illuminate the path towards developing more resilient predictive frameworks, thereby equipping stakeholders with invaluable and sustainable tools to navigate the complications of contemporary economical landscapes. Methodology. We adopt a quantitative research methodology, employing Random Forest algorithm to develop our predictive model, and leveraging the ensemble of decision trees to capture complex interactions among variables. We utilize historical financial data from a sample of Italian companies. The dataset includes a comprehensive range of financial indicators, such as liquidity ratios, profitability metrics, and leverage ratios, and non-financial indicators. Research limitations. The main limitation of this study is the reliance on historical financial data, which may not fully capture dynamic market conditions and macroeconomic factors. Additionally, the predictive accuracy of Random Forest algorithm may be influenced by the quality and completeness of the dataset. Another limitation pertains to the veracity of financial data. It is essential to acknowledge that in some instances, accounting practices aimed at masking financial distress may be employed. Managerial implications. The implications of our findings are profound for managers tasked with safeguarding organizational solvency and shareholder value. By embracing Random Forest-based predictive analytics, managers can proactively identify and address financial vulnerabilities, minimizing the risk of insolvency and maximizing long-term stability. Enhanced predictive accuracy also empowers managers to allocate resources more efficiently. Originality of the paper. While previous research has explored various machine learning techniques for predictive analytics, scant attention has been directed specifically towards leveraging and improving Random Forest to assess the insolvency risk inherent within Italian firms. Our study lays the groundwork for the integration of innovative and AI-based insolvency prediction methodologies in Italy. Furthermore, results provide valuable information for strategic decision-making, advancing the understanding and implementation of predictive analytics within this domain.