Agentic AI in Indian Financial Services: Regulatory Gaps in the PA Master Directions and the Case for Anticipatory Governance

AI-Driven Financial Advisory: from Overcoming Information Asymmetry to New Risks and Regulatory Challenges

The article analyzes the Bank of Russia's initiative to create a specialized AI agent for selecting financial products, which was announced in April 2026. It examines architectural and regulatory approaches to implementing agentic systems in financial advisory services. Based on a synthesis of international and Russian research, the study explores the economic nature of AI intermediation as a mechanism for overcoming information asymmetry, which simultaneously creates new challenges: technical vulnerabilities of algorithms, institutional risks, user interest manipulation, market latency, and the problem of liability distribution. Special attention is paid to the evolution of regulatory approaches abroad, the identification of factors determining the success of agentic systems implementation, as well as the analysis of international precedents. The author proposes a classification of maturity levels for AI financial intermediaries. Recommendations are formulated for the governance of the AI agent aimed at balancing technological autonomy, fiduciary responsibility, and competitive neutrality.

Governing Agentic AI in FinTech

Financial institutions are delegating consequential decisions to agentic AI systems that decompose goals, coordinate models and tools, and act with little oversight. Yet agentic AI governance in FinTech is under-investigated. We argue the binding governance constraint is not capability but verifiability. We define the Verifiability Gap as the shortfall between the verification delegated authority demands and the explainability and reproducibility retained after a decision. It is indexed to a verifier, evidentiary standard, and audit lag. We develop a multilevel governance theory for agentic AI and test its mechanisms in three studies over nine model versions, from a three-billion-parameter local model to a commercial frontier system. Study 1 shows that provider releases alter historical financial actions, and that the controls replay needs belong to the provider: the frontier model rejects temperature, top_p and top_k outright and exposes no random seed. Under the tightest controls each endpoint allows, a local model reproduced 320 of 320 executions, hosted models 319 of 320 and 959 of 960. Study 2 shows that orchestration is a latent policy layer. Architecture changes final actions, and no execution record repeated in any configuration at any scale. The frontier model reproduces its own actions more often than the local ones, its record no better, and loses a comparable share of its differentiation. Capability buys a higher starting point, not auditability. Study 3 shows two deterministic credit-model versions each reproduce their current action perfectly, yet the current cannot recover a historical one. We conceptualize reproducibility as a governance profile, not a scalar, yielding evidence-contingent delegation: authority is defensible only while retained evidence substantiates its exercise. Beyond finance, the framework extends to other high-stakes domains requiring auditability.

AI Governance for Institutional Readiness in Finance

This work proposes a four-layer framework (Policy, Engineering, Composition, Systemic) grounded in two distinct kinds of evidence, kept explicitly separate, and provides a 90-day implementation sequence spanning trading and payments/customer-facing systems.

<scp>AI</scp> washing

AI washing, the practice of misrepresenting the use or scope of artificial intelligence in goods or services to attract investors and gain competitive advantages, raises distinct regulatory challenges requiring the adaptation of traditional securities laws to novel technological contexts. This Article provides the first comprehensive comparative analysis of AI washing regulation across United States and European Union jurisdictions, and identifies fundamental differences in regulatory approaches on both sides of the Atlantic: the United States employs market‐based enforcement through existing securities laws with penalties up to $225,000, while the EU has adopted comprehensive ex ante regulation through the AI Act, with penalties up to €35 million or 7% of global turnover. Analyzing SEC enforcement actions, EU implementation patterns, and corporate governance implications, this Article demonstrates that effective AI governance requires selective convergence through alignment on key regulatory elements, rather than complete harmonization. The Article makes several original contributions in its systematic analysis of materiality standards for AI disclosures, its examination of board oversight duties for technological risks under Delaware law, its comprehensive assessment of the AI Act's corporate governance implications, and its practical recommendations for multinational compliance strategies. The regulatory frameworks developed for AI washing provide essential precedents for broader technology governance challenges, establishing principles for balancing innovation promotion with investor protection in an era of rapid technological transformation.

Before agentic AI scales in government: the democratic authorization gap

Some governments have begun to procure and test artificial intelligence systems that can pursue goals through connected actions. However, publicly documented evidence of mature agentic AI in public administration remains limited, with most initiatives remaining at procurement, pilot, or early deployment stages. This Perspective introduces the democratic authorization gap, defined as a break or attenuation in the demonstrable chain connecting legally and democratically grounded public authority to actions selected, sequenced, or executed by an AI agent. Drawing on democratic delegation, accountability, administrative law, and recent agentic-AI scholarship, the article distinguishes this prospective, authority-based problem from responsibility gaps and technical authorization. It identifies four mechanisms through which the gap may develop: mandate translation, recursive delegation, action diffusion, and contestability lag. Five governance conditions are proposed for the pilot stage: bounded authorization, permission inheritance, action-level traceability, named institutional responsibility, and operational interruption with reversible redress. The argument is anticipatory rather than empirical. Following the Collingridge dilemma, limited evidence before large-scale deployment provides a reason to establish governance conditions while institutional choices remain open.

Perceived regulatory clarity and the depth of enterprise AI adoption: anticipatory evidence from the EU Artificial Intelligence Act