Closing the reconciliation Gaps: How Agentic AI transforms legacy workflows

Even with sophisticated platforms like BlackRock Aladdin, SimCorp Dimension, or S&P’s Wall Street Office (WSO)1, asset and wealth management firms continue to grapple with a stubborn challenge: reconciliation breaks and the manual workarounds they force onto operations teams.

Reconciliation, matching records of cash, assets, and loans across internal systems and external sources, remains one of the most persistent and costly operational hurdles in the industry. The question is: why do these breaks persist in an era of advanced technology?

The answer lies in the limits of legacy automation. Most of today’s systems represent what might be called digital labor. They follow predefined rules, accelerate repetitive tasks, and reduce clerical burden, but they don’t truly think. When faced with exceptions, nuances, or data that falls outside the script, these systems stall and wait for human input. The result is a patchwork of manual interventions, spreadsheet gymnastics, and costly operational drag.

This is where a new paradigm is emerging. Agentic AI; AI with the capacity to act with autonomy, is reshaping reconciliation from the ground up. Unlike traditional tools that simply accelerate human workflows, agentic AI reimagines them entirely. Think of it as a skilled digital colleague: one that can interpret context, weigh alternatives, make decisions, and improve continuously with each reconciliation scenario.

The impact is profound. Instead of papering over cracks in legacy systems, agentic AI proactively fills them. It doesn’t just speed up the process; it changes the nature of the process itself, unlocking operational resilience, scalability, and intelligence at a level that legacy automation could never reach.

For firms still mired in reconciliation pain points, this shift represents more than an incremental efficiency play. It’s a strategic inflection point: a chance to move beyond digital labor into truly intelligent operations.

The gaps legacy systems leave unresolved

Even so-called best-of-breed platforms have blind spots in reconciliation. Systems like SimCorp, Aladdin, and WSO excel at handling standardized, repeatable processes, but they often falter when confronted with the realities of complex, dynamic scenarios.

The result? Gaps emerge where the platforms were never designed to operate. Exceptions pile up. Teams are forced into manual interventions, spreadsheet workarounds, and costly error resolution. These aren’t edge cases; they’re the day-to-day operational frictions that undermine scale and efficiency.

1. Voluntary corporate actions:

• Legacy platforms do not offer comprehensive automation for optional events such as dividends, rights issues, or tenders.

• Operations teams continue to manually record custodian notices, elections, and outcomes in spreadsheets.

• Missed events or late elections can lead to million-dollar losses.

• Discrepancies in entitlements or posting dates are common and are usually resolved through emails and phone calls outside the system.

2. Agent bank notices (Loan operations)

• Syndicated loan workflows in WSO depend heavily on manual processes:

  • Agent banks send notices via PDFs, emails, or faxes in non-standard formats.

  • Teams manually extract rates, paydowns, and fees, then update systems.

• OCR and RPA tools struggle with inconsistent formats.

• High manual dependency leads to errors, delays, and frequent cash breaks.

• 65% of institutions offshore loan operations and 38% use third-party services to manage notice volumes - a costly but unsustainable workaround.

3. Ambiguous FX rates and pricing discrepancies

• Different FX sources or valuation timings (e.g., 4 PM vs 5 PM rates) trigger false breaks.

• Legacy systems depend on fixed tolerance rules, leading to false positives that need manual review.

• Approximately 25% of companies identify cross-currency transaction matching as their main reconciliation challenge.

• Without context-aware processing, teams spend time reviewing harmless variances manually.

4. Reference data and static data issues

• A large portion of breaks is caused by inconsistent or incorrect reference data:

  • Mismatched security IDs (ISIN vs local ticker).

  • Outdated attributes like coupon rates or corporate actions.

  • Human errors like misbookings or decimal mistakes.

• Legacy systems flag mismatches but don’t explain root causes.

• Manual data exports to Excel for cleansing and alignment are common, which raises the risk of fragmentation and errors.

5. Timing and cut-off differences

• Legacy tools operate on batch-driven schedules:

  • Late trades or corporate action proceeds often appear in one system on T and another on T+1.

  • Temporary mismatches flood exception queues until timing aligns.

• There’s no built-in context awareness (e.g., “expected later” rules)

• Analysts manually classify and dismiss these “false alarms,” increasing workload and fatigue.

• Surveys show 32% of ops teams rank exception handling among their most time-consuming tasks.

Traditional systems have many gaps. They were built for predictable, stable processes where data is perfect and events are simple. But in today's finance world, complex exceptions are common, not rare. This leads to fragmentation, as teams rely on email, Excel, and informal knowledge to resolve issues, which increases operational risks and slows down operations.

Before moving on, it’s worth emphasizing a key limitation underlying all the above: static rules and rigid workflows. Legacy reconciliation tools depend on predefined matching rules, tolerances, and data mappings. These work for known scenarios, but they crumble when something new or messy comes along. They also tend to over-trigger alerts (false breaks) because they can’t intelligently differentiate context or severity.

 From automation to autonomy: What is Agentic AI?

Reconciliation in asset and wealth management remains vital, but is constrained by legacy systems such as SimCorp, Aladdin, and WSO, which leave significant gaps. These limitations force highly skilled professionals to act as “human middleware”, manually bridging breaks - resulting in higher costs, greater risks, and inefficiency.

Agentic AI offers a transformative solution by enabling autonomous, intelligent agents that orchestrate reconciliation workflows seamlessly across systems and data sources:

Key benefits

• Higher automation and efficiency
   Up to 94% automated matching and 90% faster financial closes, with exception handling fully managed by AI instead of manual teams.

• Enhanced accuracy and control
  Intelligent agents reconcile data consistently, provide audit-ready workflows, and proactively detect discrepancies before escalation.

• Scalable operations
  As transactions and asset classes expand, operations scale smoothly without proportionate increases in staff, which is crucial in margin-constrained environments.

• Strategic talent redeployment
  Analysts are freed from repetitive exception handling to concentrate on client service, innovation, and risk management.

• Industry momentum
   Market leaders like BlackRock are embedding agentic AI in Aladdin (e.g., the “Copilot” framework), signaling a clear shift towards autonomous operations.

Adoption strategy

Start small → validate impact → scale progressively. Agentic AI augments, not replaces, existing platforms - orchestrating intelligent, continuous workflows on top of legacy systems.

Strategic impact

Forward-looking firms are adopting agentic reconciliation to advance beyond mere cost savings into real-time control, proactive risk management, and faster decision-making - transforming reconciliation from a reactive process into a strategic differentiator.

The future of reconciliation is agentic, autonomous, and always-on - ensuring no transaction, event, or discrepancy slips through the cracks while empowering teams to concentrate on growth and innovation.

How Agentic AI fills the gaps in reconciliation

Now, let’s connect the dots: how does this multi-agent, context-aware, self-learning approach actually address the specific reconciliation gaps we identified? Below, we examine each of those problem areas and explain how an agentic AI solution can fill the gaps left by legacy systems.

1. Voluntary corporate actions (From Manual Tracking → Intelligent Orchestration)

• Traditional challenge: Manual tracking of custodian notices, elections, and outcomes; high risk of missing deadlines and making costly mistakes.

• Agentic AI advantage:

  • Uses NLP-driven agents to parse custodian/broker feeds, emails, SWIFT messages, and PDFs 24/7.

  • Logs and cross-verifies key event details (e.g., dates, options, deadlines) into a unified knowledge base.

  • Suggests default election options based on historical patterns and nudges portfolio managers for pending decisions.

  • Verifies positions and cash movements automatically post-execution.

• Impact: Minimizes operational risk, removes reliance on spreadsheets, and guarantees that no corporate action is overlooked.

2. Agent bank loan notices (From Lagged Inputs → Real-Time Loan Ops)

• Traditional challenge: Loan notices from agent banks arrive in inconsistent PDF and email formats; manual extraction into WSO leads to delays and errors.

• Agentic AI advantage:

  • Ingestion agents classify notices (principal repayment, rate reset, amendment, etc.).

  • Extraction agents parse unstructured PDFs/emails using NLP + computer vision to achieve near-human-level accuracy.

  • Validation agents cross-check extracted data with internal loan books.

  • Update agents' auto-post changes into WSO or loan management systems in near real-time.

• Impact: Allows same-day processing for approximately 99% of notices, eliminates backlogs, enhances P&L accuracy, and manages volume spikes without extra staff.

  
  

3. FX and pricing discrepancies (From False Breaks → Intelligent Tolerance)

• Traditional Challenge: Static tolerance rules in legacy systems create floods of false positives for FX and price mismatches.

• Agentic AI advantage:

  • Market data agents fetch real-time FX rates and prices from Bloomberg/Reuters.

  • Uses context-aware variance modeling to distinguish benign fluctuations (e.g., 0.5% expected FX difference) from real issues.

  • Auto-resolves or postpones breaks until final pricing aligns.

• Impact: Reduces unnecessary exceptions by 85%; shortens reconciliation time by 30% and enhances operational focus.

4. Reference data and data quality (From Reactive Fixes → Self-Healing Systems)

• Traditional challenge: Breaks often arise from mismatched or outdated reference data, requiring manual intervention.

• Agentic AI advantage:

  • Reference agents continuously reconcile identifiers, coupon rates, and static attributes across systems.

  • Auto-maps missing or inconsistent IDs using ML models trained on historical data.

  • Leverages break pattern analytics to diagnose recurring causes and proactively apply fixes.

  • Learns to handle novel asset classes and data attributes dynamically.

• Impact: Reduces downstream breaks by 40% and establishes a continuously improving, self-healing data ecosystem.

5. Timing and workflow orchestration (From Batch-Based → Continuous Reconciliation)

• Traditional challenge: Legacy systems execute end-of-day or month-end batch jobs, causing delays and buildup of exceptions.

• Agentic AI advantage:

  • Enables real-time, event-driven reconciliation as trades, settlements, and corporate actions occur.

  • Scheduling agents trigger reconciliations the moment new data arrives.

  • Flags pending items intelligently rather than labeling them as breaks.

• Impact:

  • Achieves 50% fewer suspense entries.

  • Reduces reconciliation cycle times by 30%.

  • Gives CFOs and risk officers real-time portfolio visibility.

In summary, agentic AI directly tackles the limitations of traditional reconciliation: it comprehends context by distinguishing genuine issues from noise, learns and adapts to minimize repeats and manage new cases, and operates autonomously to reduce reliance on human schedules and intervention. Together, these capabilities lead to higher match rates, quicker resolutions, and significantly fewer exceptions requiring human review.

Checklist for adopting Agentic AI in reconciliation

For leaders and practitioners eager to explore agentic AI, it’s smart to begin with a systematic assessment. Below is a checklist of key considerations; basically, a template to evaluate your readiness and determine what you need. (You can also refer to the matrix above while reviewing this checklist.)

• Identify pain point breaks: List the reconciliation break types that cause the most pain (e.g., high volume or high risk). Is it cash breaks from timing? Corporate actions? FX differences? Prioritize these for automation - agentic AI performs best when pointed at defined problem areas with lots of exception data to learn from.

  
  

• Map current responsibilities: Identify which system or team currently handles each break category. How many FTEs are involved in tasks like corporate action reconciliation? Are entire teams offshore handling loan notices? This mapping will reveal potential efficiency improvements, such as replacing a process where five people work all day with AI agents.

  
  

• Assess data Inputs and gaps: List the data sources needed for reconciliation, such as trade files, custodian statements, agent notices, etc. Are they structured or unstructured? How often are they available? This will help determine the types of AI tools required, like document-reading NLP agents or API integration agents, and where to apply them. Also, identify recurring data quality issues, such as missing or inconsistent fields, as these are opportunities for AI-driven data enrichment.

  
  

• Evaluate legacy system flexibility: Understand what your current systems can and cannot do. For example, can Aladdin ingest an external FX rate feed intraday? Can SimCorp trigger an alert on a missing corporate action? Often, the answer is no - and that’s fine because the AI layer will handle it - but knowing these limits helps design the AI workflow (the AI might sit outside and then push entries into the system via API).

  
  

• Define success metrics: Define what success looks like. Is it a percentage increase in auto-matching rate? A decrease in daily breaks? Faster close times? For example, aim for “auto-resolve 80% of FX discrepancies within the same day” or “reduce manual break volume by 50% in Quarter 1." Clear metrics will guide the project and help secure stakeholder buy-in when those targets are reached (or exceeded).

  
  

• Start with a focused pilot: Select one or two pilot categories for agentic AI, preferably those with high volume and limited scope, such as cash reconciliation or a specific asset class. Make sure you have historical data to train the AI agents, like past reconciliation records and resolutions, which are invaluable. A targeted pilot can deliver quick wins and lessons that can be applied to other areas.

  
  

• Involve operations experts: Your ops teams’ tribal knowledge is extremely valuable. Involve them in training the AI - for example, let them validate the agent’s suggested actions during the pilot. Their feedback will refine the AI’s rules. Also, prepare them for a transition from doers to overseers; change management and training are essential so they trust the AI co-worker and know how to interact with it (e.g., handling escalations).

  
  

• Ensure robust governance: As you introduce autonomous agents, set up guardrails. Define which decisions agents can make autonomously versus when human sign-off is needed (especially in early stages). Establish monitoring: dashboards that show what agents are doing, and an audit trail of actions (this eases audit and compliance concerns). Many firms find it useful to designate a “digital operations controller” role to keep an eye on the AI, though, unlike managing people, this is more about exception management than micromanagement.

  
  

• Plan integration points: Decide how the AI agents will integrate with your IT landscape. This could be via APIs into your PMS/ERP, or even using RPA for legacy systems with no API (though APIs are preferable). Think about where the “source of truth” will reside - usually, the AI updates the system of record rather than being a separate ledger. Integration also means security considerations: ensure data privacy and compliance when AI accesses sensitive financial data.

  
  

• Iterate and expand: After a successful pilot, develop a roadmap for integrating agentic AI into more reconciliation areas. Use the momentum to address more complex issues or to advance from partial to full automation. The advantage of agentic systems is that they can scale horizontally, adding more agents for new tasks, relatively easily once the initial architecture is in place. Continuously monitor performance and optimize the agents; for instance, if an agent’s accuracy on parsing notices is 92% and not improving, consider providing more training data or refining its model. Ongoing improvement should be part of the culture around this new tool.

By running through this checklist, organizations can ensure they cover the critical bases when moving to an agentic AI-powered reconciliation process. It helps to align stakeholders (operations, IT, risk, leadership) on what’s needed and sets the stage for a smooth implementation that delivers strong ROI.

Conclusion

Reconciliation in asset and wealth management is essential, but it is limited by legacy systems, such as SimCorp, Aladdin, and WSO, which create significant operational gaps. These constraints force highly skilled professionals to act as “human middleware,” manually bridging gaps across processes, raising costs, risks, and inefficiencies.

Agentic AI provides a transformative solution. By deploying autonomous, intelligent agents, firms can move from reactive, batch-based reconciliation to continuous, adaptive, and self-healing operations:

• Higher automation and efficiency: Firms deploying agentic AI achieve up to 94% automated matching and 90% faster financial closes, replacing armies of analysts with AI-managed exception handling.

• Enhanced accuracy and control: Intelligent agents execute reconciliations consistently, provide audit-ready exception workflows, and reduce operational risk by proactively catching discrepancies before they escalate.

• Scalable operations: As transaction volumes and asset classes grow, operations scale without proportional increases in headcount; a critical competitive advantage in a margin-compressed environment.

• Strategic redeployment of talent: Freed from repetitive exception management, teams can focus on client servicing, risk strategy, and innovation.

• Industry momentum: Even leaders like BlackRock are embedding agentic AI into Aladdin (e.g., “Copilot”) to drive operational agility, reflecting an industry-wide shift towards autonomous operations.
  
  

The way forward involves a cautious, step-by-step adoption plan: begin with small initiatives, verify their effectiveness, and expand gradually. Agentic AI doesn’t replace existing systems but enhances them by coordinating intelligent workflows across various data sources and platforms.

For progressive companies, agentic reconciliation offers benefits beyond cost reductions. It provides real-time oversight, proactive risk control, and faster decision-making, transforming reconciliation from a mere reactive process into a strategic edge.

The future of reconciliation is agentic, autonomous, and continuously operational; ensuring that no transaction, event, or discrepancy is overlooked, while also freeing up teams to focus on growth and innovation.

References:

1. How Agentic AI Reduces Errors in Financial Accounting - Avkalan.ai

2. Rethinking the Loan Notice process with AI management

3. When reconciliation breaks: Mastering exception management - Kani Payments

4. How Agentic AI is Reducing Risks and Costs in Balance Sheet Reconciliation | Order to Cash Knowledge Center

5. BlackRock’s Aladdin Copilot Uses AI for Investment Management

6. How Agentic AI is Reducing Risks and Costs in Balance Sheet Reconciliation | Order to Cash Knowledge Center

7. How Agentic AI is Reducing Risks and Costs in Balance Sheet Reconciliation | Order to Cash Knowledge Center