Economic and legal approaches to the humanization of FinTech in the economy of artificial intelligence through the integration of blockchain into ESG Finance

The study addresses how finance, as a key economic infrastructure, can be humanized by incorporating social and environmental considerations alongside economic criteria in investment decision-making. With the rise of Industry 4.0 and AI-driven FinTech, the paper explores the risks of misallocation (Type I and II errors) when only economic metrics are used and emphasizes the role of ESG principles in humanizing finance. The purpose is to analyze modern experience and prospects for humanizing FinTech in the AI economy via blockchain integration into ESG finance. The authors pose four research questions and hypotheses: RQ1/H1: Public institutions (state regulation factors) largely determine the development of ESG finance. RQ2/H2: The spread of blockchain technologies in society makes a limited (moderate) contribution to ESG finance development in the AI economy. RQ3/H3: Government regulation factors can have a negative impact on blockchain finance, potentially retarding its development. RQ4/H4: ESG finance provides comprehensive implications across all 17 UN SDGs. The study aims to clarify causal relationships at the intersection of FinTech, AI, and sustainable finance and to propose an economic-legal approach for policy and corporate governance.

FinTech is framed as high-tech finance involving electronic payments and advanced automation. The humanization concept entails ESG finance that avoids negative impacts on society and the environment and provides positive benefits, while maintaining economic viability (G component). Prior works discuss CSR, institutional environments, and international experiences in FinTech humanization. Blockchain’s role in supply chain finance (smart contracts, transparency, risk assessment via IoT and deep learning) is noted as a growing trend in the AI economy. Despite a well-developed conceptual base, the literature lacks clarity on causal relationships driving FinTech humanization via ESG and blockchain; this gap motivates the current analysis and hypothesis testing.

The study employs structural equation modeling (SEM) supported by correlation and regression analyses to capture complex causal pathways and both direct and indirect effects. Data comprise annual indicators for 2021–2022 across 118 countries without missing values. Key variables: bcn (percentage of crypto owners of the population, Triple A, 2022); ESG (ESG Index, Risk Indexes, 2022); governance/state regulation factors for 2021: gr1 Rule of law (WIPO), gr2 Index of Economic Freedom (Heritage Foundation), gr3 Regulatory quality (WIPO), gr4 Political and operational stability (WIPO), gr5 Government effectiveness (WIPO), gr6 Government’s online service/e-government development (WIPO); UN SDG performance indicators (Goals 1–17, 2022). Governance indicators are on a 0–100 scale from the “Humanization of economic growth... 2022” dataset. The sample’s economic-geographic structure covers all UN regions. For each research task, specific regressions are estimated and validated with F-tests where applicable, and results are integrated into a SEM to account for errors and multiple interrelations. Models estimated: ESG = F(gr1–gr6), ESG = F(bcn), bcn = F(gr1–gr6), and SDG1–17 = F(ESG).

- RQ1 (ESG = F(gr1–gr6), multiple linear regression): ESG = 65.5809 − 0.0140*gr1 + 0.3558*gr2 − 0.4268*gr3 − 0.2214*gr4 − 0.1651*gr5 + 0.0189*gr6. Model reliability: Multiple R = 0.8867, R² = 0.7863, F(6,112) = 68.08, p < 0.01. Interpretation: ESG finance development is positively associated with economic freedom (gr2) and e-government development (gr6), but higher rule of law, regulatory quality, political/operational stability, and government effectiveness are associated with lower ESG performance. Confirms H1: state regulation factors largely determine ESG finance and may constrain it. - RQ2 (ESG = F(bcn), simple regression): ESG = 36.0120 + 0.7650*bcn. Model reliability: Multiple R = 0.1702, R² = 0.0290, F(1,116) = 3.4622, p ≈ 0.065 (α = 0.10). Interpretation: A 1% increase in crypto ownership associates with a 0.765-point increase in ESG Index; contribution is moderate/limited. Confirms H2. - RQ3 (bcn = F(gr1–gr6), multiple regression): bcn = 4.4215 − 0.0249*gr1 − 0.0491*gr2 − 0.0218*gr3 − 0.0287*gr4 + 0.0440*gr5 + 0.0544*gr6. Model reliability: Multiple R = 0.2718, R² = 0.0739; significance level ~0.20; F-test not applicable at standard thresholds, indicating limited reliability. Interpretation: Blockchain finance is positively linked to government effectiveness and e-government, but negatively linked to rule of law, economic freedom, regulatory quality, and stability; overall regulatory impact can be negative or zero. Confirms H3. - RQ4 (SDGk = F(ESG), k = 1..17): Most regressions are significant at α = 0.01 with high R (several close to or exceeding 0.8), except SDG14 which shows no reliable link. Signs are predominantly negative, indicating that higher ESG Index associates with lower scores on most SDGs; exceptions noted where SDG12 and SDG13 show positive links and SDG14 shows zero link. Confirms H4: ESG finance has complex, system-wide implications across all 17 SDGs, often negative under current conditions.

The integrated SEM demonstrates a stable system of relationships linking public institutions, blockchain adoption, ESG finance, and sustainable development outcomes. Findings indicate that institutional factors mediate ESG finance strongly but currently tend to inhibit it, suggesting the need for institutional reform. Market-driven diffusion of advanced technologies like blockchain alone is insufficient for FinTech humanization; governance quality and design matter. Public institutions can inadvertently retard blockchain finance through certain regulatory dimensions (e.g., stringent rule of law frameworks, stability-oriented controls, or regulatory quality/economic freedom settings), pointing to “institutional traps.” The broad and often negative associations between ESG finance and many SDGs suggest contradictions in existing ESG practices or metrics and potential formalistic implementation, requiring recalibration of policy and governance to align ESG finance more effectively with sustainable development. The proposed economic-legal approach emphasizes improving institutional and regulatory environments, enabling targeted state and corporate measures guided by the SEM for higher precision in policy and management.

The study analyzes international experience and reveals causal mechanisms in humanizing FinTech within the AI economy via blockchain-integrated ESG finance. Key conclusions: (1) ESG finance development is largely mediated by state regulatory factors (Multiple R ≈ 0.8867), which currently tend to inhibit ESG finance, necessitating institutional improvements; (2) Blockchain diffusion contributes only moderately to ESG finance (Multiple R ≈ 0.1702), indicating market insufficiency and the need for supportive institutional environments; (3) Public institutions can negatively affect blockchain finance (cumulative impact Multiple R ≈ 0.2718), creating institutional traps that should be addressed; (4) ESG finance affects all 17 SDGs systemically, with strong but mostly negative associations, except positive links for SDG12 and SDG13 and a zero link for SDG14. Policy implication: adopt an economic-legal approach to enhance institutional support for FinTech humanization. The SEM and regressions provide tools for forecasting blockchain-based ESG finance development and for precise policy planning. Future work should probe national specificities, reconcile ESG-SDG contradictions, and develop detailed, region-sensitive recommendations for implementing the proposed approach.

The study offers global, generalized conclusions based on 118 countries and 2021–2022 data, without deep national or regional customization. The negative associations between ESG finance and many SDGs highlight potential contradictions in institutional environments and possible formalistic ESG implementation, warranting further investigation. The bcn governance model shows limited reliability (α ≈ 0.20), suggesting caution in interpreting regulatory effects on blockchain finance. Future research should: (1) analyze country/region-specific contexts; (2) refine ESG and SDG measurement linkages; (3) design actionable, tailored regulatory frameworks to overcome market failures and institutional traps.