Investment operations teams allocate excessive time to manual, repetitive tasks that delay client service, increase error rates, and prevent strategic focus on portfolio management and business development. Trade reconciliation errors cost firms millions annually, while manual NAV calculations extend reporting cycles and increase operational risk. The solution lies in the strategic use of robotic process automation (RPA).
The global robotic process automation market is expanding from $35.27 billion in 2026 to $247.34 billion by 2035; the banking, financial services, and insurance sectors are leading adoption at 36.52% of market revenue, Precedence Research reports. What’s more, leading institutions are achieving 90% reductions in manual processing errors while reallocating staff to higher-value client advisory and portfolio analysis activities, according to Global Banking & Finance Review.
This article addresses the critical elements of successful RPA implementation: identifying use cases, supporting governance, integrating legacy systems, and creating strategies for workforce transitions.
The Operational Crisis: Manual Processes at Breaking Point
Investment operations teams face mounting pressure as manual processes consume resources needed for portfolio management and business development. Investment firms of all sizes, from boutique hedge funds to large asset managers, face high volumes of manual tasks. These often include:
- know your customers (KYC) verification
- loan processing
- account reconciliation
- compliance reporting
They also face rising regulatory demands for accurate audit trails and faster processing. The financial impact extends beyond costs: manual data entry errors compromise regulatory compliance, while delayed processing extends customer onboarding timelines from days to hours.
The market signals urgency, especially as competitors adapt. Companies implementing robotic process automation see 3x-10x ROI returns within 12 months, FinTech Weekly reports. Financial institutions using automation reduce compliance errors by 90%, cut compliance task time by half, and achieve 60-70% reductions in processing time for critical workflows, FinTech Weekly and Precedence Research report, respectively. The question is not whether to adopt RPA, but how quickly and effectively organizations can implement it.
Understanding where to begin requires identifying processes where automation delivers maximum ROI while strengthening controls.
High-Value Automation Use Cases
There are countless opportunities for automation in investment operations. For example:
- Trade reconciliation
- NAV calculation
- regulatory reporting
- client onboarding
- vendor invoice processing
These represent some of the highest-priority areas. Financial entities deploying RPA in these areas have realized annual cost savings in the millions, Global Banking reports.
Contract Intelligence at JPMorgan Chase
JPMorgan Chase’s Contract Intelligence platform demonstrates this potential. In their case, AI-enhanced RPA analyzes thousands of contracts within seconds—work that previously required approximately 360,000 labor hours annually, Global Banking reports. This capability demonstrates how automation adapts to complex, unstructured data including regulatory documents, trade confirmations, and client communications.
Additional use cases include:
- automated trade settlement reconciliation across custodians and internal systems
- NAV calculation and reconciliation addressing shadow accounting challenges
- KYC and AML compliance automation
- regulatory reporting and filing
- client onboarding documentation processing
Each delivers measurable efficiency gains while strengthening operational controls.
Establishing Bot Governance: Controls That Satisfy Regulators and Auditors
Effective RPA implementation requires robust governance frameworks. Each framework applies specific functions and requirements to bots. For example, real-time monitoring systems track bot performance, identify anomalies, and alert operations teams to exceptions.
Frameworks may include:
- defining permissions
- monitoring automated activities
- handling exceptions appropriately
- maintaining audit trails for regulatory examination
Here are two key steps organizations can take to ensure proper governance and regulatory compliance.
I. Recording Robotic Process Automation Actions
Every RPA action should be recorded in an immutable audit trail with four critical attributes:
- integrity through system-generated records
- security that prevents tampering
- accuracy in recording all actions
- completeness without gaps
By embedding compliance into automation frameworks—including permissions management, activity logging, and documentation capabilities—financial institutions can ensure regulators understand how compliance decisions were made.
II. Define Escalation Procedures
Investment firms must define clear escalation procedures for complex scenarios requiring human judgment. Situations may involve:
- material exceptions
- counterparty disputes
- regulatory interpretations
In each of these cases, automated decisions may carry unacceptable risk. The automated approach creates comprehensive documentation that simplifies audits, where every step is logged and retrievable. This transparency strengthens regulator relationships while supporting internal governance.
Legacy System Integration: Robotic Process Automation Without Expensive Replacements
Investment operations rely on diverse platforms, some of which may have accumulated over decades. For example, on-premises solutions hold 68.13% of market revenue in 2025, Precedence Research reports. This may reflect some enterprises’ preference for enhanced data security and regulatory compliance, but also a dependency on legacy systems and an inability to adapt.
Preferences for Hybrid Models
Increasingly, financial institutions are shifting to hybrid models. These combine on-premises infrastructure for sensitive data with cloud-based scalability for non-critical processes. Their focus on bridging legacy systems creates new opportunities for RPA adoption. Services include:
- Consulting for process assessment
- ROI analysis
- Implementation involving workflow mapping and bot configuration
- Ongoing support for maintenance and optimization
However, the integration challenge is real: 53% of finance firms identify integration with existing systems as a primary barrier to AI adoption, Global Banking reports.
APIs and Middleware for Integration
Fortunately, API gateways and integration middleware provide standardized interfaces between RPA platforms and legacy systems. Attended RPA—where bots assist human workers in real-time—proves particularly valuable when full system integration proves impractical. This approach complements legacy applications without requiring extensive integration work.
Workforce Transition and Change Management
Technology implementation succeeds or fails based on workforce adoption. Investment operations teams must embrace automation and transition to higher-value analytical and client service responsibilities through comprehensive change management.
Engage Staff and Enhance Their Roles
Organizations must address employee concerns directly. Rather than viewing automation as a replacement, successful implementations emphasize how technology enhances roles. Clear communication about how staff will be redeployed to strategic activities builds support. Early engagement with operational teams—including them in process redesign discussions—creates ownership and improves implementation success.
Train and Upskill for Tangible Results
Training and upskilling programs should assess current team capabilities across technical, analytical, and communication skills. Classroom instruction, online learning, and hands-on practice delivered in phases ensure employees gain confidence and proficiency. Pairing staff with experienced users of robotic process automation provides guidance and support during transition.
Prioritize Satisfaction and Metrics
Organizations should establish clear performance metrics that include employee satisfaction with training programs, completion rates, productivity impact, and client outcomes. In a case study about De Nederlandsche Bank, published by Harvard Data Science Review, the firm found client satisfaction averaging 8 out of 10 or higher; combined with effective collaboration across multiple divisions, all indications pointed towards a successful adoption. These tangible metrics help measure whether change management initiatives achieve intended outcomes.
Implementation Roadmap and Performance Metrics
Financial firms should adopt a phased approach to RPA adoption and implementation. They must also define and measure key metrics for implementation success. Here, we provide recommendations for how to begin and see this through.
Phase 1: Process Assessment & Prioritization
Begin by analyzing existing processes to eliminate inefficiencies before automation. Focus on rule-based, repetitive, and time-consuming tasks rather than attempting to automate everything.
Assessment should evaluate ROI for each use case, develop a strategy, and create implementation roadmaps. Process identification examines factors including volume and frequency, rule-based versus judgment-based decisions, data availability, integration complexity, and regulatory requirements.
Processes with high volumes of structured data and well-defined rules typically offer the highest ROI and fastest payback. Organizations can achieve end-to-end automation for operations through RPA platforms combined with business process optimization and AI elements.
Phase 2: Implementation & Deployment
Key elements of implementation will include:
- workflow mapping
- bot configuration
- integration testing
- production deployment
Comprehensive testing from functional checks to integration validation ensures smooth deployment while minimizing risks. Organizations choosing between building proprietary capabilities and leveraging third-party platforms often adopt hybrid approaches using established vendors while developing custom automation for unique processes.
Phase 3: Performance Monitoring and Optimization
Organizations require concrete metrics to evaluate RPA effectiveness. Processing time reduction represents the most visible benefit (e.g., reductions in time for KYC and loan approval workflows).
Error rates provide another critical measure: financial entities can achieve near-zero data entry errors when deploying RPA. Cost reduction remains a primary driver, where many firms see substantial reductions in operational costs related to manual processing in the first year.
Metrics for success that firms can track include:
- percentage of automated activities with complete audit trails
- time required to produce audit documentation
- regulatory examination findings related to automated processes
Each of these metrics can help firms assess both operational efficiency and compliance impact.
Making Talent the Primary Outcome of Robotic Process Automation
Current RPA implementations represent the foundation for more sophisticated automation. RPA bots are integrating with artificial intelligence and machine learning to handle complex, data-driven processes requiring natural language processing and predictive analytics. Financial institutions are pursuing hyperautomation strategies combining RPA with process mining, analytics, and AI to achieve end-to-end transformation as well.
These organizations that implement RPA strategically can achieve the greatest efficiency gains without operational risk. They can:
- Reduce processing times
- Cut operational costs
- eliminate data entry errors
- strengthen regulatory compliance
Most importantly, they free talented professionals from repetitive manual tasks. Instead, they can focus on portfolio management, client relationships, business development, and other responsibilities, all of which require contextual awareness, personability, and expertise.
The competitive gap is widening. Firms that continue relying on manual processes face mounting pressure from automated competitors who process trades faster, calculate NAV more accurately, and allocate talent to client-facing activities rather than data entry. Investment operations leaders must move decisively—not with wholesale transformation, but through strategic pilots that demonstrate ROI and build organizational capability.
The institutions that implement RPA thoughtfully—targeting high-impact use cases, establishing robust governance, and genuinely engaging operations teams—will define operational excellence in investment management for the next decade.
Partner with Option One Technologies for Your Robotic Process Automation Initiatives
Option One Technologies’ managed IT and cloud platform services ensure automation delivers measurable ROI while maintaining the controls essential for regulatory compliance and fiduciary oversight. Contact one of our cloud integration experts to learn more.
