Bridging Information Asymmetry through AI-driven FinTech: The Role of Digital Footprint Analytics in Financial Inclusion

Abstract

Financial inclusion—broadly defined as the availability and equality of opportunities to access financial services—is widely recognized as critical for fostering economic growth, reducing poverty, and promoting equitable development (Berg, T., Burg, V., Gombović, A., & Puri, M., 2020). Nevertheless, despite global initiatives aimed at expanding financial access, a substantial number of individuals and small businesses, particularly in developing countries, remain excluded from traditional financial systems due to insufficient credit histories and inadequate financial documentation (Demirgüç-Kunt et al., 2022). Central to this issue is information asymmetry, a longstanding theoretical challenge articulated by foundational economic theories, including those of Akerlof (1970) and Stiglitz & Weiss (1981). These indicate how asymmetrical information between borrowers and lenders generates adverse selection and moral hazard, ultimately resulting in credit rationing and the systematic exclusion of otherwise creditworthy but information-poor segments of society.In recent years, the rapid development of financial technology (FinTech) powered by artificial intelligence (AI) has fundamentally reshaped the possibilities for overcoming informational barriers. Unlike traditional credit assessment methodologies that depend heavily on structured financial data such as credit bureau reports, income verification, and collateral evaluations, emerging AI-driven credit scoring systems incorporate large-scale behavioral data—often termed “digital footprints”—derived from non-traditional sources including smartphone metadata, social media interactions, e-commerce behaviors, and even geolocation patterns (Berg, T., Burg, V., Gombović, A., & Puri, M., 2020). Recent empirical studies have demonstrated that these novel data sources can outperform traditional financial data in predicting loan repayment behavior, thus substantially reducing information asymmetry and enabling lenders to extend financial services to previously underserved groups (Berg et al., 2020). Leading fintech companies such as Tala in the United States (which primarily serves Southern Africa and Southeast Asia) and Sesame Credit in China’s Ant Financial Services Group exemplify the transformative potential of AI-driven financial innovation. Tala, for instance, utilizes machine learning algorithms that analyze smartphone usage patterns to reliably estimate creditworthiness, enabling real-time unsecured loan approvals for individuals with no formal credit histories (Björkegren, D., & Grissen, D., 2019). Similarly, Zhima Credit has leveraged diverse behavioral indicators—ranging from online transaction consistency to social interaction networks—to deliver precise risk assessments, thereby broadening access not only to credit but also to various consumer services (Zhang, Q., & Li, X. 2023). These case studies highlight how digital footprint analytics can be broadly applied to help mitigate adverse selection and significantly reduce financial exclusion.Despite the transformative benefits, the integration of AI into credit assessment systems raises profound ethical and regulatory concerns. Critical issues include the opacity of algorithmic decision-making processes (“black box” models), the potential perpetuation of existing biases and inequalities embedded in historical datasets, and the privacy implications of intensive personal data use (Raghavan, M., Barocas, S., Kleinberg, J., & Levy, K. 2020). For instance, recent research has highlighted the unintended amplification of gender bias in AI-driven financial services, wherein ostensibly neutral algorithms disproportionately disadvantage women due to embedded socio-economic inequalities within training data (Arora & Gupta, 2025). Addressing these challenges requires robust governance frameworks, algorithmic transparency standards, and informed regulatory oversight, such as those advocated by recent developments in the European Union’s General Data Protection Regulation (GDPR) and emerging algorithmic fairness guidelines (Binns, 2024). Building upon these insights, this paper critically examines the role of AI in bridging information asymmetry within FinTech, with an emphasis on how digital footprints and behavioral analytics are reshaping credit access and financial inclusion. By synthesizing theoretical perspectives on asymmetric information with cutting-edge empirical evidence from recent studies and practical case analyses, this research aims to elucidate both the opportunities and limitations inherent in the AI-enabled transformation of financial decision-making. Furthermore, the paper offers actionable policy recommendations designed to balance technological innovation with ethical responsibility, alongside clearly defined directions for future interdisciplinary research in economics, data science, and regulatory policy.Building on existing theories, this study further incorporates the “Rice Theory” (Talhelm et al., 2014; Dong et al., 2024), which argues that cultural orientations influenced by agricultural practices (particularly rice farming) affect individuals’ social behaviors and cooperative tendencies. Applying this theory to financial technology (FinTech) adoption, we propose that users from collectivist cultural backgrounds—commonly associated with regions historically reliant on rice farming—may exhibit distinct patterns of interaction and acceptance toward digital financial services, thereby influencing the degree of information asymmetry and financial inclusion outcomes within FinTech ecosystems.