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Corporate actions modernisation
08 Jul 2026
Jonny Ruck, co-founder at Intelliactions, looks at why the next stage of modernisation is not replacing the industry’s data agenda, but is making that agenda useful by connecting data quality to economic materiality
Image: nuchao/stock.adobe.com
Corporate actions have long been one of the most complex, manual, and resource-intensive areas of asset servicing. The industry has spent years trying to improve the quality, consistency and timeliness of corporate-actions data — and rightly so. Without accurate data, no operating model can scale safely.
But better data is not the end goal. It is the foundation.
The real question is what firms do with that data once they have it. Does it simply help them process more events more quickly? Or does it help them make better decisions about which events carry the greatest economic value, which require enhanced review, and where operational expertise should be focused?
The Depository Trust and Clearing Corporation (DTCC) and The ValueExchange estimate that, for US securities alone, the industry spends approximately US$58 billion annually processing corporate actions, with a potential US$15 billion opportunity available through greater standardisation, improved data practices, and golden source data.
DTCC also notes that only 4 out of 10 voluntary corporate action events currently achieve straight-through processing (STP) because of operational complexity and data inefficiencies.
These figures underline the scale of the challenge. Corporate actions are not a marginal back office issue. They represent a major industry cost base, a significant operational burden and, in many cases, a source of material economic risk.
The industry’s response has understandably focused on better data. Golden-source models, ISO 20022, standardisation, APIs and automation are all essential parts of the answer. But better data alone does not resolve the deeper challenge: how should firms decide where to apply their operational attention?
A golden source can help identify what the event is. It does not, by itself, answer which events matter most.
A visibility gap, not a competence gap
This is not a criticism of corporate-actions teams. Quite the opposite. Corporate-actions professionals manage one of the most complex and time-sensitive areas of post-trade processing. They work under deadline pressure, across multiple markets and intermediaries, often with fragmented data, inconsistent market practices and significant client expectations.
In many cases, operations teams understand the risk better than anyone because they see the practical consequences of flawed data, compressed deadlines and manual intervention every day.
The issue is not capability. It is visibility.
Many operating models do not yet provide corporate actions teams with a structured view of the economic value attached to each event. Workflows are often driven by event type, deadline proximity, exception status, client instruction and market practice.
These are necessary controls, but they do not always show the potential financial consequence if an event is mishandled.
A low-value routine event and a high-value voluntary event may both require processing. But the consequence of getting them wrong can be dramatically different.
A missed election, incorrect default option, flawed data point, misunderstood conversion right or delayed instruction can create very different outcomes depending on the position size, event terms and market value involved.
If teams cannot see that value clearly, they cannot always use it to drive prioritisation.
What the front office already takes for granted
I have seen this divide from both sides of the same desk. Earlier in my career I processed corporate actions in the back office, working the deadlines, data gaps, and default options that come with every event. Later, I traded corporate actions arbitrage in the front office, where the same events were assessed purely on economic exposure and potential return.
The events themselves did not change between those two seats. What changed was how clearly the risk was framed, measured and prioritised.
In the front office, economic exposure drives behavior.
A trading desk does not treat a US$50,000 exposure in the same way as a US$500 million exposure. Portfolio managers, traders, credit teams, and market-risk functions all understand that risk must be measured, ranked, and escalated according to potential financial impact.
The point is not that the front office is more sophisticated than operations. The point is that the front office is usually given clearer tools to see and quantify economic exposure.
Having sat on both sides of that line, I can say the underlying risk in a corporate action does not actually change depending on where it is viewed from. A mishandled election carries the same economic consequence whether it is caught in operations or in a trading book. What differs is only how visible that consequence is made to the person responsible for acting on it.
Corporate actions teams should be supported in the same way.
If an operational error can create the same financial consequence as a trading error, then the operating model should help teams identify where that consequence is greatest. The objective is not to question the expertise of operations teams. It is to ensure that their expertise is supported by the same economic visibility that other risk-sensitive functions already rely on.
That is why corporate actions should increasingly be viewed not only as a processing function, but as an economic-risk function.
The pressure is increasing
The need for a more value-aware model is becoming more urgent.
Broadridge and The ValueExchange’s 2025 asset-servicing research found that asset-servicing volumes are growing by more than 25 per cent, while data issues remain one of the largest causes of errors. The research shows that up to 67 per cent of errors are driven by data issues, and that system spend is reducing for 41 per cent of respondents, even as volumes and operational pressure continue to rise.
That creates a difficult operating environment: more events, more complexity, more data risk, more manual intervention, tighter budgets and higher client expectations.
In that environment, asking teams simply to process more events with the same level of manual intensity is not sustainable.
Nor is it fair to expect operations teams to absorb increasing complexity without giving them better tools to distinguish where their expertise is most needed.
The industry needs a better way to allocate effort.
Operational intensity should reflect economic materiality
The principle should be simple: operational intensity should be proportionate to economic exposure.
This does not mean low-value events are ignored. It does not mean firms reduce standards or take shortcuts.
It means that enhanced review, senior escalation, data validation and manual intervention should be directed first toward the events where the potential financial consequence of failure is greatest.
That requires three connected layers.
First, the industry still needs better data. Standardised, timely and reliable data is the foundation for any improved operating model.
Second, firms need better data confidence. It is not enough to know what the data says; firms also need to know how reliable it is. Where multiple sources disagree, where terms are incomplete, or where documents are complex, those events should attract greater operational attention.
Third, firms need better value visibility. They need to understand which events carry the greatest economic exposure by looking at potential missed value, loss exposure, position size, event complexity, deadlines, optionality and client impact.
The future model should bring these layers together. Better data should not simply feed a faster workflow. It should support better prioritisation.
Voluntary events make the case clearest
The argument is especially strong for voluntary corporate actions.
Voluntary events often involve elections, deadlines, market-specific procedures, defaults, proration, conditional terms and client-level decision-making.
They are more difficult to automate and often require more human judgment. That is consistent with DTCC’s finding that only 4 out of 10 voluntary corporate action events achieve STP.
But not every voluntary event carries the same economic consequence. Some may have limited value impact. Others may involve significant optionality, large positions, complex election mechanics or high client sensitivity.
Treating all voluntary events with the same operational intensity is therefore inefficient and potentially risky.
The same logic applies across scrip dividends, tender offers, exchange offers, rights issues, Dutch auctions, merger elections, redemptions, convertible bond events, and other complex voluntary or elective processes.
In each case, the operational question should not only be ‘what is the deadline?’, it should also be ‘what is the economic consequence if this event is mishandled?’
That question moves corporate actions from a purely administrative workflow into a risk prioritisation discipline.
Making the data agenda useful
The industry is right to pursue better data. But data quality should not be viewed as the final destination. The next stage of modernisation is not replacing the industry’s data agenda. It is making that agenda useful by connecting data quality to economic materiality.
Cleaner data tells firms what is happening. Value visibility tells firms what matters.
When those two things are combined, operations teams can make better decisions about where to apply human expertise, where to escalate, where to validate, and where a lighter-touch process may be appropriate.
That is how firms reduce cost and risk at the same time.
For years, the industry has asked ‘how do we process corporate actions more efficiently?’
That question remains important. But it is no longer enough.
The next question should be ‘how do we prioritise corporate actions according to economic materiality?’
This is a governance question as much as a technology question. It affects resource allocation, risk management, client service, controls, escalation, and accountability.
Conclusion
Corporate actions modernisation is entering a new phase.
The industry still needs better data, greater standardisation and more automation. Those remain critical. But better data only becomes transformative when it changes the way firms manage risk and allocate operational effort.
This is not about replacing human expertise. It is about supporting it.
Corporate actions teams already understand the complexity of the events they manage. The next step is to give those teams clearer visibility of the value at risk, so that their expertise can be focused where it has the greatest impact.
Not every event is economically equal. Not every event requires the same level of operational intensity. And not every event carries the same consequence if something goes wrong.
The next generation of asset servicing will not be defined only by cleaner data. It will be defined by how effectively firms use that data to identify value, prioritize risk and focus human expertise where it matters most.
Better data is only the beginning. The real opportunity is turning that data into value based decision making.
But better data is not the end goal. It is the foundation.
The real question is what firms do with that data once they have it. Does it simply help them process more events more quickly? Or does it help them make better decisions about which events carry the greatest economic value, which require enhanced review, and where operational expertise should be focused?
The Depository Trust and Clearing Corporation (DTCC) and The ValueExchange estimate that, for US securities alone, the industry spends approximately US$58 billion annually processing corporate actions, with a potential US$15 billion opportunity available through greater standardisation, improved data practices, and golden source data.
DTCC also notes that only 4 out of 10 voluntary corporate action events currently achieve straight-through processing (STP) because of operational complexity and data inefficiencies.
These figures underline the scale of the challenge. Corporate actions are not a marginal back office issue. They represent a major industry cost base, a significant operational burden and, in many cases, a source of material economic risk.
The industry’s response has understandably focused on better data. Golden-source models, ISO 20022, standardisation, APIs and automation are all essential parts of the answer. But better data alone does not resolve the deeper challenge: how should firms decide where to apply their operational attention?
A golden source can help identify what the event is. It does not, by itself, answer which events matter most.
A visibility gap, not a competence gap
This is not a criticism of corporate-actions teams. Quite the opposite. Corporate-actions professionals manage one of the most complex and time-sensitive areas of post-trade processing. They work under deadline pressure, across multiple markets and intermediaries, often with fragmented data, inconsistent market practices and significant client expectations.
In many cases, operations teams understand the risk better than anyone because they see the practical consequences of flawed data, compressed deadlines and manual intervention every day.
The issue is not capability. It is visibility.
Many operating models do not yet provide corporate actions teams with a structured view of the economic value attached to each event. Workflows are often driven by event type, deadline proximity, exception status, client instruction and market practice.
These are necessary controls, but they do not always show the potential financial consequence if an event is mishandled.
A low-value routine event and a high-value voluntary event may both require processing. But the consequence of getting them wrong can be dramatically different.
A missed election, incorrect default option, flawed data point, misunderstood conversion right or delayed instruction can create very different outcomes depending on the position size, event terms and market value involved.
If teams cannot see that value clearly, they cannot always use it to drive prioritisation.
What the front office already takes for granted
I have seen this divide from both sides of the same desk. Earlier in my career I processed corporate actions in the back office, working the deadlines, data gaps, and default options that come with every event. Later, I traded corporate actions arbitrage in the front office, where the same events were assessed purely on economic exposure and potential return.
The events themselves did not change between those two seats. What changed was how clearly the risk was framed, measured and prioritised.
In the front office, economic exposure drives behavior.
A trading desk does not treat a US$50,000 exposure in the same way as a US$500 million exposure. Portfolio managers, traders, credit teams, and market-risk functions all understand that risk must be measured, ranked, and escalated according to potential financial impact.
The point is not that the front office is more sophisticated than operations. The point is that the front office is usually given clearer tools to see and quantify economic exposure.
Having sat on both sides of that line, I can say the underlying risk in a corporate action does not actually change depending on where it is viewed from. A mishandled election carries the same economic consequence whether it is caught in operations or in a trading book. What differs is only how visible that consequence is made to the person responsible for acting on it.
Corporate actions teams should be supported in the same way.
If an operational error can create the same financial consequence as a trading error, then the operating model should help teams identify where that consequence is greatest. The objective is not to question the expertise of operations teams. It is to ensure that their expertise is supported by the same economic visibility that other risk-sensitive functions already rely on.
That is why corporate actions should increasingly be viewed not only as a processing function, but as an economic-risk function.
The pressure is increasing
The need for a more value-aware model is becoming more urgent.
Broadridge and The ValueExchange’s 2025 asset-servicing research found that asset-servicing volumes are growing by more than 25 per cent, while data issues remain one of the largest causes of errors. The research shows that up to 67 per cent of errors are driven by data issues, and that system spend is reducing for 41 per cent of respondents, even as volumes and operational pressure continue to rise.
That creates a difficult operating environment: more events, more complexity, more data risk, more manual intervention, tighter budgets and higher client expectations.
In that environment, asking teams simply to process more events with the same level of manual intensity is not sustainable.
Nor is it fair to expect operations teams to absorb increasing complexity without giving them better tools to distinguish where their expertise is most needed.
The industry needs a better way to allocate effort.
Operational intensity should reflect economic materiality
The principle should be simple: operational intensity should be proportionate to economic exposure.
This does not mean low-value events are ignored. It does not mean firms reduce standards or take shortcuts.
It means that enhanced review, senior escalation, data validation and manual intervention should be directed first toward the events where the potential financial consequence of failure is greatest.
That requires three connected layers.
First, the industry still needs better data. Standardised, timely and reliable data is the foundation for any improved operating model.
Second, firms need better data confidence. It is not enough to know what the data says; firms also need to know how reliable it is. Where multiple sources disagree, where terms are incomplete, or where documents are complex, those events should attract greater operational attention.
Third, firms need better value visibility. They need to understand which events carry the greatest economic exposure by looking at potential missed value, loss exposure, position size, event complexity, deadlines, optionality and client impact.
The future model should bring these layers together. Better data should not simply feed a faster workflow. It should support better prioritisation.
Voluntary events make the case clearest
The argument is especially strong for voluntary corporate actions.
Voluntary events often involve elections, deadlines, market-specific procedures, defaults, proration, conditional terms and client-level decision-making.
They are more difficult to automate and often require more human judgment. That is consistent with DTCC’s finding that only 4 out of 10 voluntary corporate action events achieve STP.
But not every voluntary event carries the same economic consequence. Some may have limited value impact. Others may involve significant optionality, large positions, complex election mechanics or high client sensitivity.
Treating all voluntary events with the same operational intensity is therefore inefficient and potentially risky.
The same logic applies across scrip dividends, tender offers, exchange offers, rights issues, Dutch auctions, merger elections, redemptions, convertible bond events, and other complex voluntary or elective processes.
In each case, the operational question should not only be ‘what is the deadline?’, it should also be ‘what is the economic consequence if this event is mishandled?’
That question moves corporate actions from a purely administrative workflow into a risk prioritisation discipline.
Making the data agenda useful
The industry is right to pursue better data. But data quality should not be viewed as the final destination. The next stage of modernisation is not replacing the industry’s data agenda. It is making that agenda useful by connecting data quality to economic materiality.
Cleaner data tells firms what is happening. Value visibility tells firms what matters.
When those two things are combined, operations teams can make better decisions about where to apply human expertise, where to escalate, where to validate, and where a lighter-touch process may be appropriate.
That is how firms reduce cost and risk at the same time.
For years, the industry has asked ‘how do we process corporate actions more efficiently?’
That question remains important. But it is no longer enough.
The next question should be ‘how do we prioritise corporate actions according to economic materiality?’
This is a governance question as much as a technology question. It affects resource allocation, risk management, client service, controls, escalation, and accountability.
Conclusion
Corporate actions modernisation is entering a new phase.
The industry still needs better data, greater standardisation and more automation. Those remain critical. But better data only becomes transformative when it changes the way firms manage risk and allocate operational effort.
This is not about replacing human expertise. It is about supporting it.
Corporate actions teams already understand the complexity of the events they manage. The next step is to give those teams clearer visibility of the value at risk, so that their expertise can be focused where it has the greatest impact.
Not every event is economically equal. Not every event requires the same level of operational intensity. And not every event carries the same consequence if something goes wrong.
The next generation of asset servicing will not be defined only by cleaner data. It will be defined by how effectively firms use that data to identify value, prioritize risk and focus human expertise where it matters most.
Better data is only the beginning. The real opportunity is turning that data into value based decision making.
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