Agentic AI: Transforming asset servicing

This podcast episode is based on the Deloitte Luxembourg article below and includes content generated, assisted, or edited using artificial intelligence technology. It has been reviewed by a human prior to publication. The voices featured are synthetic. This podcast is provided for general information purposes only and does not constitute any kind of professional advice rendered by Deloitte Luxembourg. Deloitte Luxembourg accepts no liability for any loss or damage whatsoever sustained by any person who uses or relies on the content of this podcast. 

Introduction

The asset servicing industry is facing unprecedented pressure on multiple fronts. Asset servicers and fund management providers must contend with shrinking delivery timelines, increasingly complex investment structures, and rising operational costs, all while remaining competitive in a rapidly evolving market. Traditional digitalization and automation efforts are no longer sufficient to meet these demands.

In this context, Agentic AI has emerged as a major focus. Representing a new generation of artificial intelligence (AI), it goes beyond simple automation by enabling systems to reason, plan, and act with context. Its rise is driving important conversations around how AI can be embedded into day-to-day operations, as well as how to address data protection, regulatory compliance, and practical constraints.

This raises a critical question for the industry: what does Agentic AI truly mean for asset servicers, and where can it deliver tangible value?

This article explores the concept of Agentic AI, examines current pain points in asset servicing, highlights practical use cases, and provides an outlook on the future of the industry.

What is agentic AI in the asset servicing industry?

Agentic AI refers to systems that can act independently to achieve defined goal, rather than simply responding to instructions. These AI agents go beyond reactive chatbots, functioning as reasoning digital assistants that understand context, plan workflows, connect to external or limit to internal data and tools for performance reasons, and execute actions accordingly.

They continuously learn from experience, build governed memory, and reflect on outcomes to improve future decisions. Operating across platforms, they can also collaborate within multi-agent ecosystems, generating insights that enhance transparency and strengthen governance throughout the investment lifecycle.

For businesses, this translates into streamline operations, reduced manual effort and errors, and greater focus on higher value activities. With bounded autonomy, clear guardrails, and human in the loop oversight, Agentic AI can rapidly analyze broader context, monitor data, adapt to changing conditions, and take actions aligned with defined policies, prospectus, and controls. Its ability to operate continuously also supports functions that benefit from 24/7 availability, including reconciliations, oversight checks, reporting, and anomaly detection.

As organizations adopt these systems, effective governance is critical. Strategic deployment, role-based access, auditability, layered approvals, and consistent oversight of the agent ecosystem ensure reliability, reduce risk, and support regulatory confidence.

The result is a pragmatic approach to AI, combining language understanding, planning, reasoning, reflection, and tool use, to deliver measurable gains in productivity and decision quality across fund administration, custody, depositary oversight, and management company operations.

Pain points across the industry

The asset servicing industry is under growing pressure from multiple directions, with operational inefficiencies weighing heavily on performance and profitability. Manual onboarding, extensive KYC and AML checks, fragmented portfolio monitoring, and labor-intensive investor reporting continue to consume disproportionate resources, with compliance alone accounting for an estimated 30 to 40% of administrative costs.

At the same time, persistent margin pressure from fund managers is further compressing economics, intensifying strain on already challenged operating models.

Recent regulatory reviews by authorities such as Luxembourg’s CSSF have also highlighted ongoing deficiencies in data quality(*), governance and risk management, driving remediation efforts and heightened supervisory scrutiny.

These challenges are compounded by the increasing complexity of overseeing ever more sophisticated fund structures:

• Alternative investment structures introduce significant operational and data challenges. A single umbrella fund may contain multiple sub-funds, each with distinct  strategies, systems, and data flows across private equity, real estate, hedge funds, or feeder–master structures, resulting in fragmented, hard to reconcile data. This is further compounded by the reliance on PDFs and investor portals, which traditional systems struggle to process and integrate.
• Traditional investment funds face similar complexity, including varying levels of sub-fund leverage, an expanding range of share classes (such as hedged classes), and increasingly sophisticated product structures designed to enhance performance.

This underscores the growing need for more robust, scalable, and technology-enabled operating models across the industry. These structural inefficiencies create clear opportunities for agentic AI to automate low-value-added tasks, integrate fragmented systems, and enable fund professionals to focus on core value drivers: client relationships and the timely, accurate delivery of fund information.

Practical use cases in the market

AI is moving from experimental pilots to scaled operational deployment across the industry:

• Fund accounting and valuation: AI automates multi-source data ingestion, reconciles breaks in real time, and detects pricing anomalies before they impact net asset value (NAV), reducing cut-off pressure and improving accuracy across the daily fund cycle.
• Transfer agency operations: AI accelerates investor onboarding through automated document validation, identity checks, sanctions screening, and transaction pattern analysis, strengthening AML and KYC effectiveness.
• Custodians and depositaries: AI-driven tools analyze transaction flows, settlement patterns, and asset movements to flag breaches of investment restrictions, enhancing oversight and reducing operational and regulatory risk.
• Management companies (ManCos): AI supports delegate oversight by continuously analyzing service providers’ key performance indicators (KPIs), breaches, and operational trends, enabling earlier issue detection and stronger governance.
• Corporate secretary support: Agents generate regulator-ready reports and board materials, reducing manual effort and compliance risk through automated drafting and validation.

In Luxembourg, several initiatives are laying the groundwork for secure AI deployment in regulated environments:  

• European institutions: Considerable progress is already visible. For example, the European Investment Bank (EIB) has achieved over €200 million in savings through AI adoption.¹
• Cloud innovation: Initiatives such as the CSSF’s Clarence Sovereign Cloud are enabling secure, scalable AI hosting, providing a strong foundation for asset servicers to develop and deploy AI solutions.²

Over the next two to three years, adoption of agentic AI in Luxembourg is expected to accelerate. Initial use cases will likely focus on compliance, reporting, and reconciliation, before expanding into investor servicing, fund administration workflows, and broader operational oversight.

However, moving from proof of concept to successful implementation requires more than technology; it demands strong data governance. Firms will need to prioritize robust change management, build and continuously train internal capabilities, and establish dedicated roles to monitor and continuously improve processes and data quality as these technologies evolve.

Next evolution of asset servicing

The regulatory environment is evolving rapidly, making agentic AI not just an opportunity but an operational necessity. The EU AI Act will classify most asset servicing tools as high-risk, requiring explainable, auditable, and rigorously governed systems where transparency and continuous oversight are non-negotiable. 

The CSSF has already signaled heightened expectations through thematic reviews and white papers, reinforcing the need for stronger systems, enhanced transparency, robust governance, and disciplined data management across all operational layers. Compliance with emerging AI regulations will soon shift from a competitive advantage to baseline requirement.

The opportunity for the industry is substantial. Agentic AI can transform asset servicing by improving accuracy, enhancing client experience, and reducing costs. However, capturing this value responsibly will distinguish leaders from followers.

The path forward demands close collaboration between business and technology to build trusted, scalable solutions through iterative learning. Firms should strengthen governance capabilities, invest in workforce upskilling, and foster alignment between data science and business teams.

Agentic AI is more than a technological shift; it is a test of industry leadership. Organizations that act early and responsibly will shape the competitive standards, governance models, and ethical foundations of tomorrow’s fund industry.