Dessislav Dobrev
Senior Counsel

Dessislav Dobrev is a leading expert in artificial intelligence and the law and has spearheaded AI matters in both academia and practice. His recent book Artificial Intelligence and the Law: A Comprehensive Guide for the Legal Profession, Academia and Society, published by Thomson Reuters, has been endorsed by high-profile academics, leading law practitioners and global AI leaders. This timely volume is one of the first comprehensive studies of how AI will dramatically affect the law, both as a profession and a regulatory domain, as well as society at large.

As an Adjunct Professor Dessislav created and taught new law school courses on AI at McGill Law School, has previously taught at Georgetown Law School, and has provided seminars on AI at other leading institutions such as the Berkeley Center for Law & Technology and the University of Toronto’s Faculty of Law. In addition, Dessislav serves on the New York State Bar Association’s Task Force on Artificial Intelligence. The task force is dedicated to identifying potential benefits, dangers, and opportunities surrounding AI and to make regulatory recommendations for how to manage and integrate the technology in legal practice.

Mr. Dobrev is educated and has practiced law in both civil and common law jurisdictions and has worked in both public and private sector contexts. He is currently a Senior Counsel at the World Bank Group, where he is exposed to AI issues at the global level. Previously, he practiced corporate law at Davis Polk in New York.

INTERNATIONAL AI IN FINANCIAL SERVICES REVIEW 2026/27

This foreword explores in general terms the strategic evolution of artificial intelligence (AI) in financial services over the last year. Some of the key trends to highlight over this period include the wider adoption and integration of AI, the increased and more complex risks associated with it, and the evolving regulatory environment. Each of these will be briefly examined, as well as on what to keep a keen eye looking ahead.

Wide-Ranging Adoption and Integration of AI

AI has embarked upon a new phase within the financial services sector. The discourse is no longer centered merely on whether AI has potential value for financial institutions. It is now becoming increasingly evident that it has. Rather, attention has shifted toward how organizations can more optimally integrate increasingly sophisticated AI systems into their core business functions while maintaining effective governance, risk management and regulatory compliance. Over the past twelve months, advances in generative and agentic AI have accelerated adoption across banking, insurance, capital markets and payment systems, transforming AI from a specialized analytical tool into a broader operational capability. Recent industry research indicates that AI deployment has become widespread across financial institutions worldwide, with technology-driven firms continuing to lead traditional market participants in both experimentation and implementation. This is separate from, and in addition to, financial institutions achieving higher revenues as AI is driving demand for financing, from data centers and power infrastructure to capital markets activity (such as IPOs).

One of the most significant developments has been the emergence of AI systems capable of carrying out complex sequences of tasks with limited human intervention. Earlier deployments focused primarily on prediction and classification. Today’s systems increasingly assist with conducting compliance checks, reviewing documentation, drafting reports, supporting software development and coordinating multi-step business processes. Financial institutions are employing these capabilities to reduce operational friction, improve productivity (e.g., through improvements in internal workflows, knowledge management and operational execution) and enhance internal decision-making.

In parallel, AI is becoming increasingly embedded in market-facing activities. Financial firms are using advanced models to support portfolio management, market surveillance, anti-money laundering controls, fraud monitoring and customer engagement. In lending, richer data analysis is enabling more granular assessments of borrower characteristics, while insurers are applying AI to streamline underwriting and claims administration. Capital markets participants are leveraging AI-assisted research and market intelligence tools capable of processing vast amounts of structured and unstructured information in real time. International securities regulators have observed particularly strong growth in the use of AI for investment analysis, surveillance and regulatory compliance functions. Further, consumers worldwide are increasingly using AI to guide investment decisions.

Complex Risk Landscape

Alongside these opportunities, the risk landscape has become more complex. Concerns regarding inaccurate outputs, algorithmic bias and privacy remain relevant, but the policy debate has broadened considerably over the last year. Regulators and industry participants are increasingly focused on issues such as model concentration, dependence on external technology providers, cybersecurity vulnerabilities and the growing reliance on a limited number of cloud and foundation-model platforms. These concerns reflect the reality that AI-related risks may arise not only from the models themselves but also from the broader technological ecosystem on which financial institutions depend. The International Organization of Securities Commissions has highlighted third-party dependency, model governance and malicious uses of AI among the principal emerging concerns for financial markets.

Evolving Regulatory Environment

A notable trend over the last twelve months has been the greater involvement of international standard-setting bodies. Policymakers are increasingly approaching AI as a matter of financial resilience and institutional governance rather than merely a technology issue. The Financial Stability Board’s 2026 consultation on responsible AI adoption emphasizes enterprise-wide oversight, board accountability, risk management and lifecycle controls for AI systems. This reflects an emerging consensus that successful AI implementation requires governance frameworks capable of keeping pace with technological innovation.

Around the world, supervisory authorities are moving beyond observation and exploratory studies toward more structured expectations regarding transparency, accountability, monitoring and human oversight. Rather than creating entirely new regulatory regimes, many authorities are adapting existing prudential, consumer protection and market conduct frameworks to address AI-related challenges. The resulting trajectory suggests increasing convergence around common principles such as explainability, documentation, testing and ongoing supervision.

Looking Ahead

Some of the main risk-based trends to watch for in the next year include the following. First, there appears to be a growing sense of potential over-investment in the AI sector in certain jurisdictions. Its magnitude, dependence on debt, and circular financings raise questions about sustainability and financial stability. If the current boom turns into bust, the financial services industry will also be impacted significantly. Second, as financial services companies are widely using AI agents for common business operations, there will be a growing concern about the implications of advanced AI systems hacking into digital financial infrastructure. This is about AI agents subverting human controls and displaying unauthorized or downright deceptive behavior. Third, there currently is a widening structural gap in AI adoption, integration and knowledge as between financial institutions and regulatory and supervisory authorities. Such divergence in adoption and integration could engender a limited ability to assess AI models used by regulated entities and difficulties in monitoring emerging or fastmoving risks, thereby undermining supervisory effectiveness over time. These and other trends will determine broadly the direction of international AI in financial services going forward.