Can Client onboarding be re-imagined with Agentic AI?

Client onboarding is often misunderstood as an operational workflow. In reality, it is one of the most consequential levers in institutional banking, directly shaping how risk is managed, revenue is generated, and client relationships are established. 

Nowhere is this more evident than in Investment Banking, where onboarding is not merely a checklist; rather, it functions as a control mechanism that establishes whether a high-value client is permitted to transact, access research, participate in dealmaking, or execute trades across jurisdictions. 

Onboarding is nota gate but  a convergence point. Legal entity complexity, cross-border tax obligations, product-level restrictions, and jurisdiction-specific KYC/AML requirements all manifest in the onboarding journey. The cost of missing a step is regulatory escalation. The cost of delay is client attrition or lost mandates. 

Clients arrive with layered legal structures, bespoke product needs, and complex regulatory obligations. Internally, onboarding spans coverage teams, legal, compliance, credit, operations, and tax, all under the pressure of regulatory deadlines and client demands. 

Despite process improvements over the years, workflow tooling, centralization, and offshore support, the fundamental bottlenecks remain. Data inconsistencies, policy fragmentation, and exception-driven handoffs continue to dominate the onboarding experience. 

The unequivocal point here is - onboarding isn’t just a workflow, it’s a strategic risk lever. This is why transformation with AI or otherwise is difficult, and automation is not straightforward. In a domain this sensitive, any automation must be explainable, auditable, and aligned with control frameworks from day one. 

AI in onboarding today: Present, but peripheral 

AI is not new to onboarding, but its role in Investment Banking remains narrow and largely peripheral. Where it exists, it operates as rule-based automation or narrow machine learning embedded in specific components of the process, not as a system-wide intelligence layer.

Common deployments include:

• Name matching and fuzzy search

• Document classification

• Form validation and field mapping 

• Entity resolution

• Workflow prioritization

These capabilities are incremental, but they are not transformative. Importantly, these models operate in controlled, narrowly scoped environments, often with deterministic override mechanisms and human verification built in. They do not touch decisioning logic. They are tools, not agents.

And for most onboarding leaders, that’s intentional. Given the regulatory weight and reputational risk associated with onboarding errors, AI is permitted where it can be supervised, not where it must be trusted.

Unlocking new frontiers with GenAI and agentic AI; Precision is key 

While traditional AI has improved classification and validation tasks at the edges, GenAI and Agentic AI introduce a different class of possibility: language understanding, contextual synthesis, and autonomous orchestration.

Gen AI is well-suited to:

• Pre-onboarding research

• Document summarization

• Client memo document preparation 

• Internal policy lookup

• Drafting client communication

These use cases reduce analyst cycle time without disrupting process integrity. 

Agentic AI brings:

• Dynamic task orchestration

• Exception pattern recognition

• Case progression nudging

However, agentic orchestration introduces accountability gaps. Until agentic systems can demonstrate full traceability and alignment with control frameworks, their role will remain exploratory.

Dealing with the evaluation of GenAI and agentic AI outcomes: A structural mismatch in financial services 

One of the most underappreciated challenges in deploying GenAI and agentic AI within onboarding is evaluation, not of the model’s raw performance, but rather the assessment of the responses and outcomes resulting from Gen and agentic AI, as well as their institutional acceptability within traditional AI/ML evaluation frameworks.

In Financial Services, output quality isn’t just about accuracy; it’s about traceability, explainability, defensibility, and regulatory alignment.

Traditional metrics like precision and recall break down, especially with the lack of a single source of truth. LLM-based evaluators lack credibility in financial organizations, given the stakes viz. privacy, client confidence and reputational risk. 
 
Agentic AI further complicates this: Why was this workflow reordered? Why was this task escalated, and to whom? Was this decision consistent with precedent and policy? 

These questions have subjective components, and a traditional evaluation system would not be sufficient to address them. 
 
Until evaluation frameworks are trusted by control stakeholders, GenAI and Agentic AI will remain confined to the assistive layer. It’s essential to note that this level of caution is entirely justified, given the confidential and sensitive nature of the client data managed in this process. 

Even if Agentic AI is applied to critical workflows, such as onboarding, a human override switch and an overall Human-in-the-Loop framework are necessary. 

Designing for adoption: What’s could work

Leading onboarding teams are seeing impact by focusing on augmentation, not automation:
 

1. Using GenAI as a bounded copilot 

   • Embedded copilots that support summarization, parsing, and internal queries within workflows. The user will retain control while gaining much needed support in manual processing tasks.
     
     

2. Agentic workflows, not agentic decisions

   • Internal orchestration only, with no client-facing autonomy: agentic AI led workflows can be embedded in workflows to ease the handling of processes and manage volumes better. However, these need to be deeply aligned with a Human-in-the-Loop (HITL) framework to ensure governance of the decision-making system. 
     
     

3. Building AI governance into process design

   • Feedback loops, reason codes, and defined AI-safe zones: governance is integral to the possibility of success for embedding Agentic AI in onboarding. Organizations need to ensure there is a tangible structure that includes data governance, responsible AI, and regulatory compliance.
     
     

4. Prioritizing data remediation before AI expansion

   • Normalizing hierarchies, standardizing mappings, and centralizing metadata: the cornerstone for the success of Agentic AI application in onboarding workflows is data. Financial organizations contend with complex data systems, resulting in a fragmented approach to AI implementation. Establishing a strong, unified data infrastructure is essential for Agentic AI to fully unlock its potential within any workflow implementation. 

Progress will emerge when AI will follow governance, not bypass it.

Conclusion: Control First, Then Intelligence

In Investment Banking, onboarding is a control function and hence cannot be  a candidate for unchecked automation. 
 
Gen AI and Agentic AI offer real promise, but only when built on consolidated data, embedded within control frameworks, and scoped to assist rather than replace. 
 
Institutions that win will be those that adopt AI responsibly: incrementally, visibly, and under governance. 
 
The future of onboarding isn’t AI-led. It’s control-led - with intelligence (through GenAI and Agentic AI) added by design.