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Automation is already part of structured post-trade workflows such as matching, messaging, settlement-instruction management and reconciliation. AI has a narrower, less uniform footprint in the available evidence: Swift has described exploring natural-language processing and a corporate-actions proof of concept, while BNY describes AI-assisted reconciliation in fund operations. Those examples do not show that post-trade operations as a whole run autonomously.
What counts as automation—and what counts as AI?
Post-trade is a chain of linked activities after a transaction is executed. Depending on the transaction and market, that chain can include matching and netting, confirmation and affirmation, settlement instructions, clearing and settlement, reconciliation, corporate actions, and cash or liquidity management.
Automation can apply rules to standardized messages and route, match or update records without requiring a person to handle every routine step. AI is a more specific category: the examples discussed here involve natural-language processing or AI-supported data processing. A workflow can be highly automated without using AI, and an AI capability can assist one task without automating the complete workflow.
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That distinction matters when assessing claims. A production service, a pilot, a proof of concept and a provider’s description of a capability are different levels of evidence.
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Where conventional post-trade automation is established
Messaging, matching and operational services
Swift’s description of standardized business flows for securities market infrastructures covers corporate-action notices, narratives, instructions, confirmations and status, as well as post-trade matching and netting, securities reconciliation, and cash and liquidity management. This documents standardized workflow categories; it does not show that every firm, transaction or exception is processed without manual work. Swift’s securities market-infrastructure service description
Matching, affirmation and settlement instructions
A 2025 SEC-filed report for the Central Matching Service Provider describes three services: CTM for post-trade matching, TradeSuite ID primarily for confirmation and affirmation, and ALERT, a database of securities, cash and collateral standing settlement instructions. The report defines straight-through processing as automation from trade execution through settlement without manual intervention. That definition describes the process category or goal; it is not evidence that every transaction follows a fully straight-through path. 2025 CMSP annual report filed with the SEC
Where AI is evidenced, and how mature the examples are
The examples below differ in workflow and maturity. Their sources do not establish comparable performance results or industry-wide adoption.
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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →| Workflow | What is described | Evidence and maturity |
|---|---|---|
| Corporate-action announcements | Standardizing and automating the sourcing of announcement data across issuers and agents | DTCC’s March 6, 2024 release described a two-phase pilot. It said the first phase, which tested automated inbound messaging, had completed in December 2023; the second phase was expected to run through the end of 2024. The release establishes the pilot and its stated objectives, not independently measured outcomes or a universal rollout. DTCC announcement |
| Corporate-action data and records | Exploring natural-language processing for automation; generating interoperable corporate-action records using AI models and oracle infrastructure | Swift’s 2024 annual review describes NLP exploration and a completed proof of concept led by Chainlink. This is exploration and proof-of-concept evidence, not proof of broad production deployment. Swift Annual Review 2024 |
| Fund-operations reconciliation and NAV | AI-assisted data ingestion, cleansing, standardization and enrichment for reconciliation; intelligent NAV used across several funds, with automation being extended for more complex funds | BNY describes these capabilities in a provider-authored article. Its account is evidence of the provider’s stated use and plans, not independent validation or an industry adoption measure. BNY on AI in fund operations |
Why corporate actions remain a useful reality check
Corporate actions involve event information that must be communicated and handled across organizations. Standardized messaging can automate predictable exchanges, but inconsistent or manually handled source data creates work before downstream processing can be relied on.
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DTCC’s March 6, 2024 announcement cited a figure from SIFMA’s Operations & Technology Committee and Ernst & Young LLP: 46% of global corporate-action event data was still published and received manually. This is a dated figure reported in that announcement, not a current industry-wide measurement. DTCC’s announcement and attribution
The pilot’s focus on automating inbound messages and standardizing data addresses an upstream dependency: downstream automation is only as dependable as the records it receives. The announcement does not report independently measured reductions in manual work or errors.
What the US move to T+1 changes operationally
For the covered US instruments, settlement moved to T+1 on May 28, 2024. SIFMA lists equities, corporate bonds, municipal bonds, unit investment trusts and financial instruments comprised of those types. DTCC, SIFMA and the Investment Company Institute described the transition as a multi-year industry coordination effort. SIFMA’s T+1 After Action Report · DTCC’s release on the report
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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteA shorter settlement cycle makes timely, standardized data and effective exception handling operationally important: parties have less time to resolve issues before settlement. It does not, by itself, mean a process uses AI or is fully automated.
Swift’s retrospective reports that, for the North American ISIN events it discusses, 92% had entitlement dates matching record dates in mid-2025, compared with 4% in 2023. The comparison is specific to Swift’s stated events and periods; it should not be generalized to every market or corporate-action event. Swift’s T+1 retrospective
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to tell whether an AI claim is meaningful
When evaluating a capability, pin down the exact task and the evidence behind the claim rather than treating “AI-powered post-trade” as a single category.
- Workflow: Is it matching, affirmation, settlement-instruction management, reconciliation, corporate-action processing or NAV?
- Function: Does it route and match by rules, extract or normalize data, interpret natural language, flag anomalies or help resolve exceptions?
- Maturity: Is it a production service, a pilot, a completed proof of concept, or a provider-described capability?
- Human role: Which routine steps proceed without intervention, and which items go to a review, approval or exception-resolution queue?
- Data foundation: What message formats, identifiers and source-quality controls support the process, and can records be exchanged across parties?
- Scope: Which asset classes, institutions, jurisdictions and settlement cycles does the claim cover?
- Evidence: Does the source report measured operational outcomes, or only objectives, product features or planned work?
The cited material documents examples at different stages, but it does not establish current adoption rates across the industry, comparative accuracy, cost savings, error reduction or realized return on investment.
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