AI in Fund Administration: Benefits and Implementation

AI is transforming fund administration by automating repetitive tasks, improving accuracy, and enhancing compliance. Here’s what you need to know:

Key Benefits:
- Cost Savings: Automates data entry, reconciliation, and reporting, increasing efficiency by 25–30%.
- Error Prevention: Reduces human errors with automated validations and monitoring.
- Compliance Automation: Streamlines regulatory tasks like AML screening and transaction monitoring.
AI Tools in Use:
- Digital Onboarding: Speeds up processes like KYC and AML checks.
- NAV Calculations: Cuts processing time from hours to minutes.
- Compliance Software: Reduces errors and improves reporting accuracy.
Implementation Steps:
1. Review current processes to identify where AI can help.
2. Choose AI tools based on security, scalability, and integration needs.
3. Train staff to work alongside AI systems.
Risks & Management:
- Protect data with encryption and access controls.
- Combine AI with human oversight for effective decision-making.
- Continuously track performance to ensure compliance and accuracy.

AI isn’t replacing humans – it’s helping fund administrators work faster, smarter, and more securely. Early adoption can give firms a competitive edge.

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Main Benefits of AI in Fund Administration

AI is reshaping fund administration by tackling common challenges and delivering measurable improvements. It streamlines operations, lowers costs, and speeds up critical tasks through automation.

Cost and Time Savings

AI takes over repetitive tasks like data entry, reconciliation, and report generation. This allows fund administrators to handle more work at a reduced cost. For instance, Robotic Process Automation (RPA) integrates bank data into accounting systems, while AI-driven chatbots manage routine client questions. These tools can boost workforce efficiency by 25–30%.

Area	Impact	Implementation Example
Data Processing	20–25% reduction in review time	FundCount‘s RPA for bank data integration
Client Support	$7.3B projected savings	Chatbots for routine inquiries
Human Capital	25–30% output increase	Automated data extraction and validation

"AI enables fund administrators to automate repetitive manual tasks such as data entry, reconciliation, and report generation. This reduces the need for human intervention to more of a review process, allowing firms to process higher volumes of work at lower costs, while still keeping the human in the loop."
– Skyler Steinke, DwellFi

In addition to saving time and money, these tools help reduce human errors.

Error Prevention

AI doesn’t just save resources – it also improves accuracy. By automating validations and monitoring, it minimizes risks. For example, BlackRock uses large language models trained on over 400,000 earnings call transcripts to predict market reactions more effectively and reduce decision-making errors.

"By using AI to automate and streamline repetitive and manual tasks, such as data extraction, validation, and reconciliation, we can reduce human errors while also improving the speed and quality of our work."
– Raj Gidvani, Chief Technology Officer, Gen II

Fewer errors and reduced costs make compliance management more efficient.

Compliance Automation

AI also revolutionizes compliance by automating complex regulatory tasks. In 2022, financial institutions faced nearly $5 billion in fines for compliance violations. AI helps reduce such risks through advanced solutions.

Compliance Area	AI Solution	Impact
AML Screening	Pattern Matching	Reduces false positives
FATCA/CRS	Automated Validation	Improves accuracy
Transaction Monitoring	Real-time Analysis	Detects suspicious activities

"AI isn’t a replacement for human expertise, it’s a tool to augment it. By automating routine tasks like transaction screening and regulatory reporting, AI allows compliance professionals to focus on high-value activities such as strategic planning and in-depth investigations. This synergy between AI and human expertise leads to more effective and efficient AML processes."
– Joseph Ibitola, Flagright

A good example is TAINA‘s platform, which automates FATCA and CRS validation and cross-references tax forms with authoritative sources.

AI Tools for Fund Administration

AI is transforming fund administration by simplifying workflows, minimizing mistakes, and increasing efficiency. Here’s how specific tools are making a difference:

Digital Onboarding Systems

AI-powered platforms are speeding up investor onboarding while ensuring regulatory compliance. For example, Mesh ID slashed onboarding time from 12 weeks to just 2 weeks. These systems automate tasks like KYC (Know Your Customer) and AML (Anti-Money Laundering) checks, making the process faster and more reliable.

NAV Calculation Tools

AI-driven tools are revolutionizing Net Asset Value (NAV) calculations. A leading custodian reduced processing time from 3–4 hours to under 5 minutes per client. They also improved compliance processes, cutting rule generation time from over a month to less than a week, with a 30% efficiency gain.

Compliance Software

Compliance tasks are another area where AI excels. One asset management group used AI to generate ESMA reports in under 2 seconds, achieving a 99% reduction in input errors and cutting remediation time by 80%. Another organization processed over 2,000 regulatory circulars annually using AI tools . Charter Group Fund Administration leverages automation to handle various fund types, including crypto and listed funds, while meeting offshore compliance standards.

These examples highlight how AI is reshaping fund administration by delivering faster, more accurate, and scalable solutions.

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How to Add AI to Fund Administration

This guide breaks down the steps to effectively integrate AI into fund administration.

Process Review and Planning

Start by evaluating your current operations to pinpoint areas where AI can have the biggest impact. Studies show AI can improve operational efficiency by 25-30% by automating processes and enhancing decision-making.

Recent data reveals that over 90% of alternative fund managers already use AI for risk and compliance tasks. To get started:

Examine your current workflows
Identify repetitive tasks and bottlenecks
Focus on processes with the best potential for return on investment (ROI)
Establish clear timelines and metrics for implementation

Once you’ve completed this review, you’re ready to choose AI tools tailored to your specific needs.

Choosing AI Solutions

Use your process audit as a foundation for selecting AI solutions that meet your operational requirements. For example, Altruist reduced client onboarding time by 91% by strategically using AI.

Selection Criteria	Key Considerations
Security	Encryption, authentication, and audit trails
Scalability	Ability to handle growth and increased workloads
Integration	Compatibility with current systems and API access
Compliance	Adherence to regulations and reporting needs
Cost-efficiency	ROI potential and maintenance costs

"AI tools can process and analyze large datasets, which can assist alternative fund managers in meeting reporting standards, analyzing market trends, portfolio risks, and identifying potential compliance issues." – Hilton Goudriaan, Head of Systems, Regulatory & Compliance at Ocorian.

Staff Training and Adoption

After implementing the right AI tools, focus on training your team to ensure smooth adoption. One mutual fund company improved training completion rates by 40% and cut training time in half using AI-driven adaptive learning and automated tracking.

"AI promises to create transformational changes in our industry, but human input will remain critical. Take algorithmic trading: the FCA requires humans to intervene in machine processes for vital checks and to stop runaway errors that could disrupt global markets." – Joe French, Managing Director and Head of Financial Crime at Ocorian.

Managing AI Implementation Risks

Integrating AI effectively requires addressing potential risks with strong strategies. With generative AI estimated to contribute $200–$340 billion in annual value, it’s clear that managing risks is a critical part of the process. Below, we outline key steps to mitigate these risks.

Data Security Measures

Protecting data is crucial for fund administrators. A solid data security framework should include enterprise-grade encryption, multi-factor authentication, continuous monitoring, and secure off-site backups. Notably, 92% of alternative fund managers are already using AI in their risk and compliance efforts.

Security Component	Implementation Requirements
Encryption	Use enterprise-grade encryption for both data at rest and in transit
Access	Enforce multi-factor authentication and role-based access controls
Audit Trails	Implement continuous monitoring and maintain detailed activity logs
Backup	Perform regular backups and store them securely off-site

"Banks are ultimately responsible for complying with BSA/AML requirements, even if they choose to use third-party models."
– Interagency Statement on Model Risk Management for Bank Systems Supporting Bank Secrecy Act/Anti-Money Laundering Compliance

Staff and AI Integration

AI implementation should enhance, not replace, human decision-making. Combining AI-driven insights with sound risk controls is key. Strategies for effective integration include:

Defining clear oversight roles for AI management
Rolling out AI systems in stages to minimize disruption
Holding regular feedback sessions with staff to identify and address challenges

"Firms must update systems and controls to counter increasingly sophisticated criminal activities."
– Joe French, Managing Director and Head of Financial Crime at Ocorian

Building strong protocols for human-AI collaboration supports better performance tracking and operational success.

Performance Tracking

Ongoing monitoring is essential for managing AI-related risks. The National Institute of Standards and Technology (NIST) AI Risk Management Framework advises a structured method for assessing AI performance and ensuring compliance with regulations. This includes annual evaluations of model accuracy, documenting improvements, validating compliance, and continuously reviewing outputs.

McKinsey experts point out that AI systems are often more complex and less transparent than traditional models, making sophisticated monitoring tools a necessity. Fund administrators should establish processes to quickly identify and correct errors, balancing the benefits of automation with the need for robust risk controls.

What’s Next for AI in Fund Administration

AI is reshaping fund administration, with managed assets expected to reach $59 billion in 2024. This is just the start of a transformative shift in the industry.

Emerging AI Technologies

AI advancements are driving measurable progress in fund administration. According to McKinsey, AI can boost data processing efficiency by up to 30%, while JPMorgan’s AI-driven cash flow model has cut manual work by 90%.

Technology	Current Efficiency Gains	Future Potential
Generative AI	27–35% improvement in front-office productivity	Streamlined investment reporting and RFP creation
Agentic AI	Limited use	Autonomous decision-making and execution
AI-Powered Analytics	Basic performance tracking	Detailed risk narratives produced in hours

"Next year, gen AI-powered investigation tools will start to collapse multi-week money laundering investigations into hours, generating comprehensive risk narratives and connection maps that reveal hidden transactional patterns invisible to traditional human analysis."

Rob Paisley, Director, SS&C Blue Prism

These advancements are setting the stage for a major shift, requiring fund administrators to plan strategically.

Preparing for the Shift

As AI continues to redefine fund administration, administrators must adapt to fully leverage its potential. By 2028, 75% of enterprises with a strategic AI platform in place are expected to see increased returns on their investments.

"Generative AI will democratize sophisticated financial planning, turning personalized, high-touch wealth management from a luxury service into a scalable, accessible solution."

Zeynep Hizir, Director, SS&C Financial Services Limited

Steps to prepare include:

Technology Integration
Seamlessly connect AI tools with existing systems to maintain transparency in decision-making. PwC notes that 81% of asset managers are exploring partnerships or mergers to strengthen their tech capabilities.
Building a Governance Framework
Companies with robust AI governance by 2028 could see 40% fewer ethical issues related to AI use.
Upskilling Teams
With over 90% of asset managers already using advanced tech tools, the focus must shift to training teams to understand and operate AI-driven systems effectively.

The future of fund administration lies in combining automation with human insight, ensuring AI supports rather than replaces critical decisions.

Conclusion

AI adoption is on the rise – 72% of organizations now rely on AI-driven tools to improve efficiency and accuracy. These tools can increase operational efficiency by up to 30% and enhance processing accuracy by 54%. Investments in AI are expected to surge, growing from $1.89 billion today to $17.12 billion by 2033.

This shift highlights a broader move toward digital transformation. The numbers tell the story: 94% of companies are engaging with AI in some way, and while 40% are already using generative AI tools, 80% plan to adopt them within the next year. As Kumar Ujjwal, Founder and CEO of DwellFi, puts it:

"Today’s AI is exceptionally adept at handling repetitive tasks, outperforming humans in specific areas. This capability is reshaping the fund administration landscape, enabling firms to achieve unprecedented levels of efficiency and scalability".

However, success with AI isn’t just about adoption – it requires solid data governance, employee training, and strong security protocols. With 71.43% of firms investing in AI tools and infrastructure, the focus is increasingly on tackling data integrity risks, which 60% of asset managers identify as a major challenge. Balancing AI’s measurable advantages with effective governance is key to transforming fund administration for the future.