📈 economics

AI-Enabled Sustainable Finance: Examining the Roles of FinTech Capability, Digital Trust, and ESG Orientation in Green Investment Decisions

This study utilizes PLS-SEM on a simulated dataset to demonstrate that FinTech and AI capabilities significantly drive green investment decisions by enhancing digital trust and ESG orientation, with financial literacy serving as a key contextual moderator.

Original authors: JAINISH BHAGAT

Published 2026-08-26

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: JAINISH BHAGAT

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

The global effort to slow climate change depends on a massive shift of money. Investors need to move their savings away from industries that harm the planet and toward those that help it. This is the promise of sustainable finance. At the same time, the financial world is changing rapidly because of new technology. Artificial intelligence and digital platforms are becoming the tools people use to manage their money. But a gap remains in our understanding: how does the technology actually convince a person to choose a green investment? It is not enough to know that the tools exist; we need to understand the mental steps a person takes when they see a digital interface and decide to buy a sustainable asset. Does the technology simply make the data available, or does it change how much the person trusts the system or how much they care about the environment?

A recent study by Jainish Bhagat at P.P. Savani University explores exactly this question. The research does not rely on asking real people questions or tracking real bank accounts. Instead, the author used a sophisticated computer simulation to create a realistic group of 500 imaginary investors. This method allowed the researcher to build a controlled environment where every variable could be measured with precision, testing how different factors interact to lead to a decision. The study focuses on a chain of events: starting with the basic technology a platform has, moving to how smart the platform's artificial intelligence is, and seeing how these factors build trust and shape a person's values before they finally click the button to invest in a green project.

The investigation begins with the foundation of the technology itself, known as FinTech capability. This refers to the basic digital infrastructure a financial platform possesses. The study found that a strong foundation is essential because it directly enables the platform to use advanced artificial intelligence. When a platform has the right technical tools, it can deploy smarter algorithms. These smart systems do more than just process numbers; they change the investor's mindset in two distinct ways. First, the clarity and speed provided by the artificial intelligence help build digital trust. When an investor sees that a system is transparent and reliable, they feel safer using it. Second, the same technology helps sharpen the investor's focus on environmental, social, and governance issues. By making complex sustainability data easy to understand, the tools help the investor align their personal values with their financial choices.

Once trust is established and the investor's focus on sustainability is sharpened, the decision to invest follows. The simulation showed that both digital trust and a strong orientation toward environmental values are powerful drivers that push people toward green investments. Interestingly, the artificial intelligence itself also had a direct influence on the decision, even without going through the other steps. This suggests that the technology acts as a catalyst on multiple levels. It builds the confidence needed to act, it clarifies the moral reasons for acting, and it directly influences the final choice. The study confirmed that these relationships are not just guesses but are statistically robust patterns within the simulated data, explaining a significant portion of why people make these specific financial choices.

A crucial discovery in the research involves the role of financial knowledge. The study split the simulated group into those with lower financial literacy and those with higher literacy to see if their decision-making paths differed. The results revealed a clear divide. For investors with less experience in finance, the primary path to a green investment was through trust. They needed the digital platform to feel safe and reliable before they would commit their money. For investors with high financial literacy, the path was different. They used the artificial intelligence tools to deeply analyze the environmental and social aspects of an investment. For them, the technology was a way to verify that their values were being met, rather than just a source of reassurance. This finding suggests that a single digital interface cannot serve all investors equally; the way technology influences a decision depends heavily on how much the investor already understands about finance.

The implications of these findings extend beyond the computer screen. For the designers of financial apps and platforms, the study offers a clear guide on how to build better tools. If the goal is to encourage green investing, the platform must be transparent enough to build trust, especially for those who are new to investing. For more experienced users, the platform should offer deep, detailed data that allows them to verify the sustainability claims themselves. The research also points to a need for better education. Since financial literacy changes how people interact with these tools, teaching investors about both finance and sustainability could help more people make informed, ethical choices. While the study relied on a simulated dataset rather than real-world transactions, the internal logic of the model was rigorously tested and validated, offering a strong blueprint for how technology, trust, and values combine to shape the future of sustainable finance.

Technical Summary: AI-Enabled Sustainable Finance

Problem Statement
The global imperative to reallocate capital toward sustainable economic activities has accelerated the growth of green finance and algorithmic financial tools. However, a critical gap exists in understanding the precise mechanisms through which technology-enabled capabilities drive individual sustainable investment decisions. While existing literature often relies on general technology adoption models (e.g., UTAUT, TAM), it frequently overlooks how specific FinTech and Artificial Intelligence (AI) infrastructures induce psychological state shifts—specifically Digital Trust and ESG Orientation—that are necessary to convert technical capability into actual capital deployment into green assets. This study addresses this gap by proposing an integrated structural model connecting technical capabilities with cognitive and attitudinal orientations to explain Green Investment Decisions (GID).

Methodology
This research employs an empirical research simulation based on a generated dataset ( $N=500$ ) designed to model realistic latent construct distributions, measurement error, and covariance structures. No human participants were recruited; the dataset serves as a statistical prototype to validate the theoretical framework and workflow prior to potential real-world data collection.

Theoretical Framework: The study integrates Dynamic Capabilities Theory (DCT) and Trust Theory. DCT frames FinTech Capability as the foundational infrastructure enabling higher-order AI Capabilities, while Trust Theory posits that decision-makers require institutional and technological trust to mitigate risk when interacting with automated systems.
Analytical Approach: The study utilizes Partial Least Squares Structural Equation Modeling (PLS-SEM).
- Measurement Model: Validated for indicator reliability, internal consistency (Cronbach's $\alpha$ , $\rho_A$ , Composite Reliability), convergent validity (AVE), and discriminant validity (Fornell-Larcker and HTMT criteria).
- Structural Model: Assessed using non-parametric bootstrapping ($5,000$ resamples) to determine path coefficients ( $\beta$ ), $t$ -statistics, $p$ -values, and effect sizes ( $f^2$ ).
- Predictive Power: Evaluated via $R^2$ , $Q^2$ , and PLSpredict (10-fold cross-validation) comparing PLS-SEM against a Linear Regression benchmark.
- Moderation: A Multigroup Analysis (PLS-MGA) was conducted to examine the moderating role of Financial Literacy, splitting the sample into Low and High literacy groups after establishing measurement invariance (MICOM).

Key Results
The structural model demonstrated robust predictive power, explaining 42.8% of the variance in Green Investment Decisions ( $R^2 = 0.428$ ).

Direct Effects:
- FinTech Capability (FC) significantly drives AI Capability (AIC) ( $\beta=0.512, p<0.001$ ).
- AI Capability positively influences both Digital Trust (DT) ( $\beta=0.421, p<0.001$ ) and ESG Orientation ( $\beta=0.384, p<0.001$ ).
- Both Digital Trust ( $\beta=0.315, p<0.001$ ) and ESG Orientation ( $\beta=0.402, p<0.001$ ) exert strong direct positive effects on Green Investment Decisions.
- AI Capability also has a significant direct effect on GID ( $\beta=0.165, p=0.001$ ).
Mediation:
- Both Digital Trust and ESG Orientation act as complementary partial mediators in the relationship between AI Capability and Green Investment Decisions. The total indirect effect is significant ( $\beta=0.287$ ), confirming that AI influences investment decisions both directly and through these psychological pathways.
Moderation (Financial Literacy):
- Low Financial Literacy Group: AI Capability drives Green Investment Decisions primarily by fostering Digital Trust (path AIC $\to$ DT $\to$ GID is stronger).
- High Financial Literacy Group: AI Capability drives Green Investment Decisions primarily by reinforcing ESG Orientation (path AIC $\to$ ESG $\to$ GID is stronger).
- This indicates a strategic divergence: less literate investors rely on platform trust to make green decisions, while highly literate investors leverage AI tools to directly evaluate ESG alignment.
Predictive Validity:
- The PLS-SEM model demonstrated high out-of-sample predictive power, with Root Mean Squared Error (RMSE) values consistently lower than the Linear Regression benchmark across all GID indicators.

Key Contributions

Theoretical Integration: The study unifies Dynamic Capabilities and Trust Theory to explain sustainable capital deployment, moving beyond general technology adoption to specific psychological mechanisms (Trust and ESG Orientation).
Empirical Validation of the AI-ESG Link: It provides evidence that AI tools do not merely process data but actively strengthen personal ESG orientation by lowering cognitive screening costs and enhancing information transparency.
Contextual Nuance: The research delineates how financial literacy acts as a boundary condition, shifting the decision-making reliance from "trust in the platform" (for low literacy) to "value alignment via analytics" (for high literacy).

Significance and Claims
The paper claims to offer a validated empirical framework for understanding how digital financial infrastructure translates into sustainable outcomes. Its significance lies in providing actionable, differentiated strategies for stakeholders:

For Platform Designers: Developers should incorporate Explainable AI (XAI) and tailored interfaces—emphasizing security and trust cues for less experienced users, while providing granular ESG analytics for sophisticated investors.
For Regulators: The findings suggest a need for algorithmic ESG standards to prevent greenwashing and the integration of sustainability concepts into digital financial literacy education.
For Asset Managers: Understanding the dual pathways (Trust vs. Orientation) allows for more effective targeting of capital toward green assets based on investor profiles.

The author maintains a modest stance regarding the study's scope, explicitly acknowledging that the findings are based on a simulated dataset. They posit that while the statistical workflow is validated, future research is required to confirm these relationships using real-world investor survey data and actual brokerage portfolio records.

Technical Summary: AI-Enabled Sustainable Finance

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