The Algorithmic Paradox: Do AI-Enabled Trading Platforms Reduce or Amplify Investment Risk for Women Retail Investors in India?

Abstract

The increase in artificial intelligence-enabled trading platforms has fundamentally transformed retail investment landscapes across India, promising democratized access, algorithmic efficiency, and data-driven decision-making. However, for women retail investors, these platforms present a paradoxical dynamic: they simultaneously offer tools that could reduce information asymmetries while potentially amplifying risk through algorithmic biases, overconfidence effects, and gendered digital divides. This conceptual paper examines the dual-edged nature of AI-enabled trading platforms for women retail investors in India, proposing a formal "Algorithmic Paradox Framework" to explain when and through what mechanisms these platforms shift from risk-reducing to risk-amplifying technology. Following Jaakkola's (2020) "Model" template for conceptual articles, this paper builds a theoretical framework and generates testable propositions. Drawing on a structured integrative review of peer-reviewed literature from Scopus-indexed journals (2015-2026), alongside regulatory data from SEBI, NSE, and AMFI, the study develops a comprehensive risk typology identifying four key risk dimensions: operational, market/portfolio, behavioral, and decision/information risk. The analysis reveals that the algorithmic paradox is resolved through the interaction of three boundary conditions: financial/digital literacy, human advisory integration, and regulatory safeguards. Platform explainability is conceptualized as a characteristic of the information environment that influences cognitive processing. The paper contributes a parsimonious, testable conceptual framework and proposes targeted regulatory and platform design interventions.