Smart Investment Insights: Applying a Logistic Regression Based Artificial Intelligence Model to Predict the Investability of Malaysian SMEs
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Universiti Malaysia Sarawak (UNIMAS)
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The emergence of Artificial Intelligence (AI) as a transformative force in financial decision-making presents new paradigms for evaluating investment potential, particularly within the context of Small and Medium-Sized Enterprises (SMEs). This study investigates the role of AI-driven risk assessment in shaping the investability of Malaysian SMEs, with a specific focus on the predictive influence of financial indicators such as profitability, liquidity, solvency, and leverage ratios. A logistic regression model commonly applied as a supervised machine learning algorithm was employed to generate the investability outcome, enabling a binary classification of SMEs based on financial health and risk exposure. Anchored in the Resource-Based View (RBV) and Stakeholder Theory, the study positions AI not merely as a technological advancement, but as a strategic asset capable of reshaping investor perceptions and access to capital. Using panel data analysis, the research explores whether AI integration enhances the explanatory power of traditional financial variables and moderates their relationship with investment outcomes. The findings reveal that while core financial metrics retain predictive strength, AI introduces both amplifying and corrective effects depending on the financial context. These insights underscore the importance of ethical algorithm design, stakeholder-aligned deployment, and data contextualization in promoting inclusive financial innovation. Ultimately, this study contributes to the evolving discourse on digital finance by establishing an empirical foundation for AI-integrated investment evaluation frameworks in emerging economies.