Banking automation: AI agent workflow for asset servicing

There is a moment that happens in most banking operations teams around record date.

Someone pulls the positions report. Someone else cross-checks trades. A third person opens the settlement status screen. Then, almost ceremonially, someone opens Excel.

And quietly, the same thought runs through the room: we just need to make sure the right person gets paid.

That is the entire job of asset servicing. Coupons. Redemptions. Corporate actions. Everything distils down to one deceptively simple question: who owns the security at the exact moment it matters?

Simple question. Painfully complex answer. And for most banking groups, the infrastructure built to answer it was designed in an era that no longer exists.

Asset servicing, as a discipline, is not broken. The logic is sound. The rules are well understood. But the execution across live, interconnected systems is where banks quietly accumulate operational cost.

Here is what the typical setup actually looks like beneath the surface:

The complexity compounds quickly. Take something as apparently straightforward as a bond coupon payment.

Hold bond, receive interest. Straightforward in principle. The operational reality is messier:

Now scale that across thousands of securities, millions of transactions, and counterparties across multiple jurisdictions.

This is not a processing task. It is a continuous decision problem. Most banking infrastructure was built for the former.

Asset servicing failures rarely announce themselves. The cracks appear as friction:

Nothing dramatic. But over time, that friction becomes operational cost, risk exposure, and a dependency on people who simply know how it works.

Industry analysis from Asset Servicing Times has been consistent on this: corporate actions processing has historically been a primary source of operational risk and potential loss for post-trade service providers. Embracing the right technology to improve operational efficiency, evaluate different data sources, and highlight exceptions will elevate some of the burden and risk, freeing up people's time to focus on complex, value-add work.

Knowing the problem is not the same as solving it.

Before any solution makes sense, it helps to be precise about the baseline.

A typical banking group running asset servicing operates across a layered stack:

Core Systems

Supporting Infrastructure

Batch schedulers and ETL pipelines, rule engines, Excel-based reconciliations, and email-driven exception handling all sit beneath these core systems, holding the operation together through a combination of scheduled jobs and manual intervention.

The critical gap: these systems do not communicate in real time.

Ownership determination, the entire foundation of asset servicing, becomes a view created after the fact rather than a continuously validated truth. Data is pulled, stitched together, manually checked, and then acted upon. By the time anyone has a clear picture of who owns what, the window for proactive intervention has often already closed.

Back in 2012 McKinsey analysis of bank back-office operations found that more than 70% of applications were paper-based, and of those, 30 to 40% contained errors that required reworking. Today most of it has moved to online but applications routinely get stuck in a single data-verification step for more than five days before moving forward. Despite global tech spending by banks exceeding $600 billion, labor productivity in major banking markets has continued to decline, not improve.

The investment is going in. The results are not coming out.

The pressure on operations teams is not just structural. It is now regulatory.

In February 2023, the SEC adopted amendments to Rule 15c6-1, shortening the standard settlement cycle for most US broker-dealer transactions from T+2 to T+1, with compliance required from May 28, 2024. The same shift occurred across North America simultaneously.

The practical consequence: every hour of manual processing time that was previously acceptable under a two-day cycle now represents operational exposure. As SS&C's post-trade analysis puts it, the move to T+1 has materially reduced the margin for operational delay. What was once manageable under longer timelines is now exposed in real time.

For asset servicing teams specifically, JP Morgan's T+1 client guidance identified several areas directly impacted: compressed timelines for ex-date and record date changes, event reconciliation and entitlement calculations on voluntary corporate action events, and cover/protect periods for tender offers. All of these demand faster ownership determination, not the same determination done slightly more quickly.

This is the context in which event-driven orchestration moves from a "nice to have" to an operational necessity.

Forward-looking operations teams are redesigning these workflows around a different principle. Not faster batch processing. Something more fundamental:

Moving from process-driven operations to event-driven orchestration.

Instead of asking how to process coupons faster, ask how to always know who owns what, at any point in time.

Those are different systems. One optimises the assembly line. The other eliminates the need for the assembly line in its current form.

This is the conceptual foundation behind an orchestration layer: a system that sits above existing platforms, listens to changes as they happen, applies ownership logic continuously, and triggers downstream actions only when something actually matters.

Research from Camunda on capital markets orchestration shows that process orchestration enables financial institutions to move beyond outdated legacy constraints by connecting disparate systems and delivering full visibility into processes, eliminating pre-trade errors and reducing exception handling time significantly. McKinsey estimates that 75 to 80% of transactional operations in banking, including general accounting and payments processing, are amenable to automation. The barrier has never been the desire. It has been the infrastructure through which automation is applied.

An orchestration layer does not replace core banking systems. It does not rip out the custody platform or rebuild the trade capture system. It sits above them, integrating, listening, orchestrating.

Lamatic.ai is built specifically for this model: a managed platform that abstracts AI orchestration infrastructure so teams can build and run multi-step workflows without rebuilding the plumbing.

Here is the high-level architecture:

Each layer deserves explanation, not as a technical specification, but as an account of why it changes operational outcomes.

Instead of pulling data once a day, the orchestration layer maintains a continuously updated view. APIs from custody and trade systems, database sync via read-only replication, SWIFT messages, and event streams where available are all ingested incrementally.

The practical outcome: a repo trade is booked at 11:47am, the orchestration layer receives the update within seconds, and ownership recalculation triggers automatically. Not at close of business. Immediately.

Lamatic's Flow builder allows these ingestion pipelines to be configured visually, without rebuilding custom data pipelines from scratch each time a new asset class or feed is added.

Instead of time-based batch triggers like "run the coupon process at 6pm", the system operates on event-based triggers:

Everything runs because something happened, not because the clock said so.

This is the operational core of asset servicing. Using coupon processing as an example, the inputs are buy, sell, and repo trades with their statuses, settlement confirmation, record date, and current position data.

The logic, defined once and transparently in the orchestration layer:

Critically, this logic is version-controlled, auditable, and transparent. Not buried in a stored procedure or a spreadsheet that one person knows how to read. Lamatic's workflow versioning ensures every change is tracked, reviewable, and reversible.

Once ownership is resolved, entitlement follows systematically.

For fixed income: coupon calculation with accrued interest adjustment. For redemptions: principal repayment allocated to correct holders. For corporate actions: position adjustments, new security issuance, fractional share handling.

The orchestration layer can pull live pricing and rates as needed and handles partial allocations natively, without manual intervention at each step.

This replaces end-of-day reconciliation as a separate cleanup step. Every output is cross-checked against source systems, expected entitlements, and previous state as part of the execution process itself.

If coupon expected equals $100,000 and allocated equals $98,230, the system flags immediately and routes to an exception workflow. The gap does not sit undiscovered until the next morning.

Exception management in most banks today means: everyone reviews everything flagged, and the team triages from there.

An orchestration layer changes this. Humans handle what genuinely requires human judgment.

Exceptions handled systematically:

What reaches the human:

Every exception follows a consistent path: detected, routed to the correct team with full context, resolution logged, workflow resumes. No institutional memory required.

Once everything is validated, outputs are pushed to downstream systems automatically. Payment instructions sent. Ledger updated. Client reports generated. No manual handoff, no triggered batch.

A concrete example illustrates the difference clearly.

The scenario: An investment-grade corporate bond, semi-annual coupon, $2M face value across multiple beneficial owners, several of which are parties to active repo agreements.

This process works. But it is reactive, labour-intensive, and creates a window between record date and resolution that is operationally exposed. Under T+1, that window is significantly narrower, and the tolerance for errors within it has shrunk accordingly.

Continuous (from trade booking onwards)

All trades, including repo entries, are continuously synced. Ownership is updated dynamically with every change. No snapshots. A live, maintained view.

When record date is reached

The system detects the event automatically. It already knows exactly who holds what at current settlement status, which positions are subject to repo agreements and who the economic owner is, and what accrued interest is owed from buyer to seller.

Ownership is already resolved

By the time "record date" appears on anyone's calendar, the calculation is already complete. The system has been maintaining it continuously.

Coupon event triggers

Entitlements are assigned. Payment instructions are prepared. Only positions with genuine ambiguity go to exception queues: genuinely unsettled trades close to the wire, disputed repo terms.

Output

Payments go out cleanly. The ledger is updated. Client reporting is generated. No scramble, no last-minute review.

Repo transactions are where most operations teams slow down. The core tension is that legal ownership and economic ownership are different things. Legal title transfers to the repo buyer. Economic interest, including coupon entitlement, stays with the seller.

Most systems handle this inconsistently because it requires joining data across trade types that live in different parts of the system. In practice this means:

The T+1 shift has intensified this. As SS&C observes, higher repo volumes, more frequent rollovers, and greater counterparty connectivity are amplifying operational intensity across the post-trade lifecycle. In many firms, the post-trade workflow remains a patchwork of inherited processes and fragmented systems that have accumulated over time, preventing any meaningful straight-through processing from being achieved in practice.

With an orchestration layer, repo is defined once, explicitly:

No ambiguity. No dependency on who in the team happens to know the repo book. The rule is encoded and applied consistently.

If repo is where teams slow down, corporate actions are where they stop.

Multiple feed sources with different interpretations, variable timing, mandatory versus voluntary elections, position adjustments that cascade across accounts, and tax implications that vary by jurisdiction. Most banks handle this through specialist teams, manual data enrichment, and interpretation meetings where senior ops staff decide how to process an event that does not fit cleanly into the rules.

The post-trade processing solution market reflects the scale of demand this creates: valued at $5.64 billion in 2024 and projected to reach $8.44 billion by 2029 at an 8.3% compound annual growth rate. The primary driver, according to the report, is the perpetual push for straight-through processing that reduces manual intervention and accelerates transaction times.

With orchestration:

As Broadridge's leadership has noted in Asset Servicing Times, AI capabilities are increasingly being used to support enquiries, provide data insights, and automate the resolution of specific exception scenarios throughout the corporate actions lifecycle. Investments in workflow tools and automated processes that remain compliant with industry and regulatory changes are vital for organisations planning for the future.

The primary assumption is that this is fundamentally about saving time. It is. But that is not where most of the value sits.

Clarity

At any point in time, you can answer: who owns what, who is entitled to what, what is pending, what is resolved. Not "as of last night's batch." Now.

Control

Instead of reacting to issues found after execution, you detect them during execution. The difference between a late reconciliation and a pre-emptive flag is significant, not just operationally but in terms of client impact and regulatory exposure.

Consistency

No dependency on specific individuals who know how it works. No tribal knowledge encoded in undocumented spreadsheets. No interpretation drift between teams. The logic is defined once, applied everywhere, and version-controlled.

The shift looks like this:

Banks have tried rule engines, workflow tools, and robotic process automation. Each has delivered value at the margins. None has solved the foundational problem because they automate steps, not decisions.

An RPA bot that pulls positions from the custody system faster does not change the fact that ownership is still being reconstructed after the fact. A rule engine that flags exceptions does not help if the underlying data it is working from is a snapshot from twelve hours ago.

The pattern is consistent. McKinsey's analysis of banking efficiency programs finds that traditional cost-reduction approaches typically deliver modest gains of between 3 and 5%, which rarely stick as other priorities emerge and costs gradually creep back up. Banks launch efficiency programs every two to three years on average, but these efforts focus on quick adjustments rather than fundamentally reducing demand or simplifying the operating model.

A purpose-built orchestration approach is structurally different:

According to Camunda and EY's trade exception management analysis, connecting AI agents, RPA, people, and APIs in governed workflows achieves 45% lower exception handling time and a 35% reduction in exception and error handling cost, without requiring a replatforming project.

A phased orchestration rollout is the practical route for any banking group. Lamatic supports this through a modular build approach that allows teams to start with a single workflow and expand incrementally.

Phase 1: Fixed Income Coupons

The most rule-based, highest-volume, most immediately impactful workflow. Automate ownership determination and entitlement allocation for fixed income coupon events. Establish the data sync layer and ownership engine. Measure against current reconciliation SLAs.

Phase 2: Redemptions

Build on the ownership engine. Redemptions are conceptually simpler but operationally complex because of last-minute trade activity and settlement delays. The event-trigger model begins resolution before maturity date, not on it.

Phase 3: Corporate Actions

The highest-complexity workflow. By Phase 3, the orchestration layer is already connected to all relevant data sources. Corporate actions become a question of defining event-specific logic and exception routing, not rebuilding infrastructure.

Teams involved throughout: Operations, Technology, Risk and Compliance work in parallel rather than in sequence. The orchestration layer needs operational knowledge to encode rules correctly, and that knowledge transfer is itself valuable independent of the technology.

The scale of opportunity is well-documented across authoritative sources:

Does an orchestration layer replace existing banking systems?

No. Platforms like Lamatic.ai sit above existing systems and integrate with them, typically via read-only ingestion for data inputs and controlled write-back for outputs. The custody platform, trade capture system, and settlement engine remain in place. The orchestration layer connects them and applies logic across them. See how Lamatic integrates with existing infrastructure.

How does this handle data security and compliance requirements?

The typical deployment model uses read-only data ingestion, with write-back operations limited to approved downstream systems. The layer can be deployed within existing bank infrastructure without data leaving controlled environments. All workflow decisions are logged with full audit trails, a significant improvement over current fragmented audit records.

What is the realistic implementation timeline?

A phased approach typically sees Phase 1 operationally deployed within a few months, with subsequent phases following at quarterly intervals depending on complexity and stakeholder engagement. The critical path is usually data access and rule validation, not the platform itself.

How does this relate to the T+1 settlement change?

The SEC's T+1 mandate significantly compresses the available time for ownership determination and exception resolution. An orchestration layer that maintains a continuous view of ownership, rather than reconstructing it at the point of processing, is directly suited to this environment. The work is effectively done before the deadline arrives.

How does it handle repo transactions specifically?

Repo logic is defined explicitly in the orchestration layer: who holds legal title, who holds economic interest, when ownership reverts. This eliminates the current ambiguity where two teams may interpret the same repo agreement differently. The rule is encoded once and applied consistently, across every coupon event and every maturity.

What is the difference between this and RPA implementations banks have already run?

RPA automates steps. Orchestration automates decisions. An RPA bot might extract positions from a custody system faster, but ownership determination still happens after the fact. An orchestration layer maintains a continuous, event-updated view of ownership, which means decisions are made from live data, not reconstructed snapshots.

Which asset classes does this approach support?

The framework applies across fixed income (coupons, redemptions), equities (dividends, corporate actions), and structured products, wherever ownership determination and entitlement calculation follow defined rules. The logic layers are asset-class agnostic at the infrastructure level; rules are defined per asset type.

Strip everything else away, and this is the core of what changes:

Before: Reconstruct a view of ownership when you need to process an event.

After: Maintain a continuously validated view of ownership, and process events as a natural consequence.

Asset servicing does not need to be reinvented. The underlying logic is sound. The rules are well understood. What needs to change is the infrastructure that executes them, specifically the shift from batch-driven, retrospective processing to event-driven, continuous orchestration.

Most banks are trying to fix asset servicing by adding more checks, hiring more people, or improving spreadsheets. McKinsey's research on banking productivity is unambiguous: these approaches do not deliver lasting gains, and the complexity of banking operations continues to grow faster than the interventions meant to contain it.

What changes outcomes is centralising decision logic, making workflows event-driven, and reducing manual dependency to situations that genuinely require human judgment.

That is what a purpose-built orchestration layer like Lamatic.ai enables. Not by replacing what exists, but by connecting it in a way that was never possible when every system ran on its own clock.

Ready to design this for your operations stack? Book a demo with the Lamatic team or start building for free.