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AI and RPA in Fund Administration

Posted on Oct 1, 2025

The investment fund industry has entered a new era of automation and intelligence. Technology now plays a central role in delivering accurate NAVs, meeting investor demands, and maintaining compliance. Two of the most talked-about innovations—Artificial Intelligence (AI) and Robotic Process Automation (RPA)—are often grouped together. While both bring efficiency, they are fundamentally different in design, application, and risk profile.

 

Understanding the Difference

Robotic Process Automation (RPA) is rule-based software that mimics repetitive human actions. It excels at structured, predictable tasks such as:

  • Transferring data between systems
  • Generating investor notices from templates
  • Reconciling transactions across accounts
  • Validating fund subscription forms

In short, RPA reduces human error and speeds up routine workflows. It is highly effective where processes are stable, rules are clear, and exceptions are minimal.

 

Artificial Intelligence (AI), on the other hand, is designed to “learn” from data and make judgments. In fund administration, AI may be applied to:

  • Identifying anomalies in NAV calculations
  • Predicting investor redemption patterns
  • Interpreting unstructured data like contracts or side letters
  • Enhancing compliance through real-time sanctions and PEP screening

Where RPA follows fixed rules, AI adapts and evolves. That flexibility makes AI far more powerful—but also more complex to implement and govern.

 

Applications in Fund Administration

Fund administrators are already seeing the benefits of combining the two. For example:

  • RPA can extract data from PDFs and populate reporting systems.
  • AI can analyze that same data set for unusual patterns that may signal an error or compliance breach.
  • AI enables plain language queries for report generation. Instead of coding or manual extraction, a fund manager could ask, “Show me redemptions over $10 million in Q2,” and the system can instantly produce an accurate report.

Together, RPA and AI create a cycle of efficiency and oversight that reduces operational risk.

For managers and investors, this means faster reporting, fewer mistakes, and a more transparent service model.

 

Security Risks to Consider

While the benefits are clear, both technologies introduce new security risks. Fund administrators must carefully manage these risks, especially in a highly regulated environment like fund administration.

 

RPA Risks:

  • Credential exposure: Bots often need system logins. If these are not managed securely, they become a weak point.
  • Process vulnerability: If an RPA script is altered or corrupted, it can produce inaccurate data at scale.
  • Auditability: Without proper monitoring, it may be difficult to trace errors back to their source.

AI Risks:

  • Data privacy: AI requires vast amounts of investor and transaction data. Any breach could expose sensitive information.
  • Confidential data access: Many AI services are cloud-hosted. If administrators fail to carefully vet AI services, they risk storing or processing sensitive fund and investor data outside their control. This creates regulatory and reputational risks.
  • Bias and misjudgment: If the training data is flawed, AI can produce inaccurate or discriminatory outcomes.
  • Explainability: Regulators and auditors require transparency. “Black-box” AI decisions can create compliance challenges.
  • Adversarial attacks: Sophisticated cyber threats can manipulate AI models, leading to compromised results.

 

How to Secure RPA in Fund Administration

Many professionals overlook RPA security because it operates on fixed rules, yet vulnerabilities remain.  Best practices include:

  • Credential management: Store bot credentials in secure vaults, never hard-coded in scripts.
  • Segregation of duties: Ensure bots cannot both initiate and approve transactions without human oversight.
  • Change controls: Monitor and review all script changes to prevent unauthorized alterations.
  • Bot activity monitoring: Track every automated transaction with timestamps and audit logs.
  • Fail-safe protocols: Build in exception handling so bots stop and alert staff rather than pushing through flawed processes.

By treating bots as “digital employees,” administrators can apply the same rigorous controls used for human staff.

 

How to Secure AI in Fund Administration

To mitigate risks around confidential data, administrators must go beyond standard cybersecurity practices:

  • Private deployment: Use on-premises or private cloud AI models where sensitive data never leaves controlled infrastructure.
  • Data minimization: Train AI on anonymized or tokenized data, reducing exposure of personally identifiable information.
  • Vendor due diligence: Assess AI providers for SOC 2 Type II compliance, GDPR alignment, and data residency safeguards.
  • Encryption everywhere: Protect data at rest, in transit, and during AI model processing.
  • Strict access controls: Limit who can interact with AI models and log every interaction for audit purposes.
  • Independent validation: Periodically test AI models to confirm accuracy, fairness, and resilience to manipulation.

These safeguards help ensure AI delivers insights without undermining trust in investor confidentiality.

 

Conclusion

AI and RPA are not interchangeable. RPA brings speed and accuracy to structured tasks, while AI introduces intelligence and adaptability to complex fund operations. Both can revolutionize fund administration—but only if paired with strong governance and security practices. Above all, protecting investor confidentiality when using AI is critical. By deploying AI securely and transparently, administrators can harness its benefits without compromising trust.

At Pinnacle Fund Services, we are constantly looking to implement process improvements using technology.  However, data security is our primary concern.  Please contact David Smith at [email protected] or 1-604-559-8921 to see how Pinnacle can automate your processes.