Artificial intelligence is no longer a supporting tool in sustainable finance. New academic evidence shows it has become a core capability shaping how ESG risks are identified, measured, and managed across global capital markets.

A newly published systematic review of academic literature finds that research linking AI and ESG investing surged to a record high in 2025, with 120 studies connected to the topic. The review highlights a clear inflection point: AI is now central to ESG integration, greenwashing detection, and sustainability-driven risk management.

Research activity remained minimal between 2018 and 2021, with fewer than five studies published annually. Momentum began to build in 2022, when publications rose to 14, then accelerated to 23 in 2023. Output jumped sharply in 2024 with 74 studies, before reaching its peak in 2025.

“This trend shows that artificial intelligence has moved from the margins to the core of sustainable finance,” said lead author Boutahir Omar, a PhD researcher in finance. “AI is now shaping how ESG risks are evaluated, how transparency is enforced, and how investment decisions are made.”

The review draws on 40 peer-reviewed studies published between 2018 and 2025, selected from an initial pool of 245 papers identified through structured academic screening.

ESG assessment and greenwashing detection

The largest share of research focused on AI-powered ESG measurement. Machine learning, natural language processing, and big data analytics were found to improve the consistency and accuracy of ESG assessments, particularly in markets where disclosure standards remain fragmented.

AI models were frequently used to identify gaps, contradictions, and misleading claims in sustainability reporting, making them a powerful tool for detecting potential greenwashing. As a result, many ESG evaluation processes are becoming increasingly automated.

Researchers emphasized that explainable and human-centered AI systems are essential to maintaining investor confidence. Clear decision logic and transparent model design were repeatedly identified as prerequisites for trust.

“AI can significantly reduce information asymmetry in ESG analysis,” said co-author Lahlou-Kassi Habiba. “But these benefits only materialize when transparency and governance are taken seriously.”

Despite advances, the review noted that inconsistent ESG metrics and uneven disclosure practices continue to limit comparability across sectors and regions.

Risk detection and resilience building

A second major research theme examined AI’s expanding role in sustainability-related risk management. Studies showed AI tools being applied to anticipate environmental, financial, and operational risks linked to climate transition, regulatory change, and resource volatility.

Advanced techniques such as predictive algorithms, time-frequency modeling, and wavelet analysis were used to assess exposure to climate policy uncertainty, energy market shocks, and systemic financial stress. These tools were found to enhance portfolio resilience and improve forward-looking risk management.

AI-driven risk detection is now being tested across manufacturing, energy systems, and financial markets. However, the review found no unified framework for AI-based sustainability risk assessment, raising concerns about consistency as adoption spreads.

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FinTech, blockchain, and sustainable finance

Another growing research area explored the integration of AI with FinTech and blockchain in green finance. Combining AI analytics with distributed ledger technology was shown to improve data traceability, verification, and accountability in sustainable investment products.

Blockchain-based systems supported ESG data validation, while AI-enhanced financial platforms improved operational efficiency and financial inclusion. These applications were especially relevant in emerging and indigenous financial markets, where trust and transparency gaps are often more pronounced.

While the convergence reduced information asymmetry between issuers, investors, and regulators, implementation challenges varied widely depending on regulatory maturity and infrastructure.

Ethics and governance challenges

Ethical and governance concerns formed a critical research cluster. Algorithmic bias, limited interpretability, and weak oversight structures were identified as persistent risks.

Several studies proposed using ESG principles themselves to audit AI systems, arguing that human-centered governance is essential to responsible deployment.

“Without strong ethical safeguards, AI could amplify the very risks ESG frameworks are designed to address,” Omar warned.

The review also highlighted uneven ESG maturity across industries, which complicates cross-sector comparisons and regulatory alignment.

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A structural transformation underway

The authors concluded that AI is fundamentally reshaping both the theory and practice of sustainable finance. Its influence now extends beyond efficiency gains to include accountability, transparency, and long-term resilience.

However, unresolved issues around data quality, model explainability, and governance could limit the technology’s long-term impact if left unaddressed.

“AI-driven sustainable finance represents a genuine paradigm shift,” Habiba said. “Its success will depend on responsible innovation and close collaboration between finance, technology, and policy.”

As ESG scrutiny intensifies worldwide, the findings suggest AI will play an increasingly decisive role in how capital is allocated and how sustainability risks are identified and managed.

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