Transforming financial services with confidence-driven Agentic AI

Agentic AI seems to offer a viable capability to automate such processes further. However, the heightened sensitivity to risk within these business processes requires a careful approach to ensure confidence in the solution is maintained. 

Confidence-driven Agentic AI presents a smarter and more intuitive approach, where systems decide their actions based on how certain they are about a given situation. The result is flexible, cautious and client-friendly AI agents – crucial in highly regulated sectors like finance.

 

What is a confidence-driven agent?

Picture a digital agent that, much like a skilled team member, constantly assesses how confident it is that the desired result will be achieved before it takes the next step. Instead of rigidly following a checklist, the agent considers factors such as client responsiveness, the importance of any missing documents and overall information completeness. In such scenarios, agentic execution can take a few different pathways. For example:

• High confidence results in automatic progression within the process
• Moderate confidence results in gentle reminders
• Low confidence, especially with higher-risk scenarios, triggers escalation to a human.

This approach aligns closely with human judgment, making automated processes feel more natural and trustworthy.

 

Confidence-driven agents in action: institutional onboarding

Imagine onboarding a new institutional client, such as an offshore investment fund. This typically involves collecting several important documents such as proof of incorporation, tax documentation and beneficial ownership details. A confidence-driven system would:

• Continually monitor document submissions and client responsiveness
• Assess its confidence level based on current data – for example, identifying when only one document is received after several days
• Decide next steps based on its confidence level; if it has low confidence, it may escalate by involving a relationship manager to engage in direct outreach to the client.

Once the client submits the missing document, confidence increases, and the process moves forward seamlessly without further delays.

 

Why confidence-driven agents matter

Confidence-driven agentic AI represents a substantial improvement over traditional automation in the following ways:

• Accelerating onboarding for responsive clients
• Reducing unnecessary follow-ups and frustration
• Promptly identifying situations needing human intervention, minimizing compliance risks
• Enhancing client experience and satisfaction.
  
  

Structuring confidence-driven agents

A helpful way to organize confidence-driven agents is through a structured framework that comprises the following three components, with examples.

1. Decision model to evaluate situations and provides recommendations:

{

 "decision_type": "Document Evaluation",

 "action": "Check completeness and predict client responsiveness"

}

2. Context information to maintain an organized record of client status and prior interactions:

{

 "client_type": "Offshore Trust",

 "received_documents": ["Proof of Incorporation"],

 "pending_documents": ["UBO Declaration", "Form W-8BEN-E"],

 "days_since_last_interaction": 3,

 "risk_level": "High",

 "confidence": 0.4

}

3. Action rules to define rules for permissible actions based on the current confidence level:

- if confidence < 0.5 and risk_level == "High":

      action: escalate_to_relationship_manager

 - if confidence >= 0.5 and confidence < 0.8:

      action: send_gentle_reminder

 - if confidence >= 0.8:

      action: proceed_to_compliance_review

This tripartite structure ensures clarity, helps teams scale automation safely, and supports compliance and audit requirements

 

Legal, compliance, and auditability considerations

Implementing confidence-driven agents also means thoroughly addressing Responsible AI considerations:

• Legal and compliance – ensuring adherence to regulatory standards, clear decision boundaries, and established procedures for human oversight
• Explainability – clearly documenting how decisions are reached, essential for maintaining compliance and building trust
• Auditability – keeping precise records of all actions and decisions to facilitate easy compliance reviews and regulatory audits.
  
  

Broad applicability across use cases

Confidence-driven agents are not limited to client onboarding. When applied correctly, it can significantly enhance various financial services operations, including:

• Know Your Customer (KYC) and Anti-Money Laundering (AML) processes – streamlining reviews and highlighting cases needing human oversight
• Customer service interactions – accurately identifying when complex situations require escalation
• Credit decisions – managing approvals based on clear evaluation of financial data completeness.
  
  

Shaping the future of financial services

Adopting confidence-driven agents combines the efficiency of automation with the nuanced judgment of experienced professionals. By developing automated processes that recognize uncertainty and respond appropriately, financial institutions can deliver safer, faster and more personalized client experiences.

Capco is helping our clients implement confidence-driven strategies in many Agentic AI implementations. We can help bring this framework to life through tailored implementation aligned with your organization’s risk frameworks goals. Our deep expertise in financial services and AI integration, ensures that confidence-driven agentic systems are designed with compliance, auditability, and business outcomes in mind. We co-develop with your teams to pilot solutions that deliver measurable improvements in efficiency, client satisfaction, and risk management.