Algorithmic Transparency and Trust in AI-Enabled FinTech: An Integrated Model of Investment Decision-Making

Abstract

The swift expansion of financial technology (FinTech) has transformed investment methods. Ordinary investors can now utilise digital investing platforms powered by artificial intelligence (AI). Despite this, there is a lack of study regarding the impact of algorithmic openness and trust in AI on investment decisions. This study examined the effects of ‘Digital Financial Literacy (DFL)’, ‘Government Support (GS)’, ‘Perceived Risk (PR)’, and ‘Perceived Algorithmic Transparency (PAT)’ on ‘FinTech Trust (FT)’, and subsequently the effects of FT on ‘Attitude (ATT)’ and Investment Decision-making (IDM). The study examined the moderating effect of AI advisory trust (AIT) on the connection among attitude and investment decision-making. The research employed a quantitative design, collecting data from 425 retail investors utilising FinTech investment platforms in India. The suggested model was analysed utilising SmartPLS 4 through Partial Least Squares Structural Equation Modelling (PLS-SEM). Findings demonstrate that digital financial literacy, governmental support, and perceived algorithmic transparency positively influence FinTech confidence, whereas perceived risk has a significant negative effect. Perceived algorithmic transparency was the most powerful indicator of FinTech trust. Moreover, FinTech trust significantly and positively affects the attitude of investors. AI advising serves as a mediator and reinforces the advantageous correlation between mindset and financial decision-making. This research enhances the Theory of Planned Behaviour by incorporating trust and explainable artificial intelligence. The research has practical ramifications for the development of transparent, reliable, and investor-focused FinTech systems.