Algorithms or Instincts? Understanding Bias in Digital Investment Platforms

Abstract

The rapid evolution of Financial Technology (FinTech) has fundamentally transformed retail investment participation worldwide. Digital trading platforms, robo-advisory systems, cryptocurrency exchanges, and AI-driven portfolio applications have increased market accessibility, reduced transaction costs, and accelerated decision-making processes. While traditional behavioral finance literature has extensively examined psychological biases such as anchoring, herding, overconfidence, risk perception, and emotional bias in conventional financial markets, limited conceptual integration exists regarding how these biases manifest within digitally mediated investment ecosystems. This conceptual paper develops a comprehensive framework integrating behavioral finance theory with digital platform characteristics and financial literacy as moderating variable. Drawing upon Prospect Theory, Heuristic Theory, and technology acceptance perspectives, the proposed model positions psychological biases and digital platform features as key determinants of digital investment decision-making, with financial literacy serving as a moderating factor. The framework contributes theoretically by extending cognitive bias analysis into digital financial contexts and practically by providing insights for FinTech developers, financial advisors and financial educators. The study highlights the importance of responsible interface design and digital financial literacy in mitigating bias amplification within FinTech environments.