Reinventing Corporate Actions with AI and Blockchain: A Strategic Transformation for Financial Services.

Corporate actions processing stands at a critical inflection point. What was once a manual, error-prone operation that consumed millions of dollars annually due to inefficiencies is now being revolutionized by the convergence of artificial intelligence and blockchain technology. A global industry survey conducted by The Value Exchange found that, on average, it costs financial services firms more to source data for a Corporate Action event than to process that information. Operational inefficiencies currently result in millions of dollars in annual costs for businesses due to errors and manual data processing.

The transformation is not theoretical—it is happening now. Major financial players, including Euroclear, Swift, UBS, Franklin Templeton, Wellington Management, CACEIS, Vontobel, and Sygnum Bank, are successfully demonstrating how AI, oracles, and blockchains can solve a decades-long challenge in unstructured data finance. The bottom line is clear: firms that embrace AI-driven automation and blockchain-enabled transparency will significantly reduce processing times, lower costs, and mitigate operational risks, while those who delay will find themselves increasingly disadvantaged in an accelerating digital marketplace.

This transformation represents more than technological advancement—it is a strategic imperative that will determine competitive positioning, operational efficiency, and risk management capabilities for the next decade.

The Corporate Actions Crisis: Understanding the Challenge

The Current State of Corporate Actions Processing

Corporate actions processing remains one of the most complex and operationally intensive functions in capital markets. A white paper by SIX found that 46% of all corporate actions data is processed manually, representing an extremely high figure for modern times, resulting in a huge expenditure of time and attendant high costs, as well as susceptibility to errors in data gathering.

The magnitude of this challenge becomes clearer when examining the fundamental nature of corporate actions. These events—ranging from dividend distributions and stock splits to mergers and spin-offs—affect millions of shareholders and require precise execution across multiple intermediaries. This fragmented and complex method of processing and disseminating Corporate Action information significantly impacts both investors and the issuers who rely on market participants and financial services organizations to distribute and process the information to the broader market.

The Cost of Inefficiency

The financial impact of current inefficiencies extends far beyond simple processing costs. The corporate actions lifecycle is time-consuming, prone to errors, and costly, with this manual approach leading to inefficiencies, increased business risks, and poor user experiences. Organizations face multiple layers of expense: data sourcing costs that exceed processing costs, extensive manual reconciliation requirements, error correction procedures, and the opportunity costs of delayed settlements.

Globally, it is estimated that 46% of event data is published and received manually, driving unnecessary risk and expense to organizations that process Corporate Action events for their clients and downstream organizations. This statistic underscores the scale of the transformation opportunity available to forward-thinking financial institutions.

Operational Complexity and Risk Factors

The current corporate actions ecosystem suffers from several fundamental challenges that create systemic inefficiencies. There are no uniform international standards or regulations to which such disclosures must conform, as most countries apply very different sets of regulations. This means that corporate actions are communicated in a variety of formats and publication types.

This lack of standardization creates multiple cascading problems. Information may be communicated through press releases, company websites, or buried within hundreds of pages of digital or print publications. Since understanding corporate actions requires specific background knowledge, experts are needed, leaving room for interpretation, which in turn can lead to errors. The result is a system where financial institutions may learn of corporate actions too late or miss them altogether, creating substantial operational and reputational risks.

The Settlement Timeline Pressure

The transition to a T+1 settlement cycle in the USA, which will probably come to Europe as well in the years ahead, doesn’t make the job any easier. Shortened settlement cycles create additional pressure on already strained manual processes, demanding even greater accuracy and speed from corporate actions teams. This timeline compression makes the need for automation not just beneficial but essential for maintaining operational viability.

AI-Powered Transformation: Revolutionizing Corporate Actions Processing

The Promise of Intelligent Automation

Artificial intelligence is fundamentally transforming how financial institutions approach corporate actions processing. Chainlink connected its decentralized oracles with large language models (LLMs) like OpenAI’s ChatGPT, Google’s Gemini and Anthropic’s Claude to extract corporate actions data from various sources and transform it into a structured format called “Golden Records” that comply with global financial standards like the ISO 20022 and the Securities Market Practice Group (SMPG) guidelines.

This transformation represents a paradigm shift from reactive to proactive processing. AI systems can interpret unstructured announcements, validate data against multiple sources, and automatically trigger appropriate workflows. The technology addresses the core challenge of corporate actions: transforming disparate, unstructured information into standardized, actionable data that can flow seamlessly through processing systems.

AI Implementation in Corporate Actions

The practical application of AI in corporate actions is transitioning from theory to operational reality. BetaNXT highlighted two specific product offerings: CastX, their voluntary corporate actions front-end that automates the entire corporate action event lifecycle to eliminate manual errors; and tax-smart optimization tools that provide real-time data on tax-loss harvesting opportunities, wash sale avoidance, and pre-trade “what-if” scenarios.

These implementations demonstrate AI’s ability to handle both routine processing and complex optimization scenarios. The technology can simultaneously manage high-volume, repetitive tasks while providing sophisticated analytics for strategic decision-making. This approach focuses on three key areas: automation to reduce manual processing errors in areas such as corporate actions, personalization that extends beyond simply adding names to emails, and actionable intelligence through AI analytics that make connections across various datasets.

Predictive Analytics: Anticipating Corporate Events

One of the most powerful applications of AI in corporate actions is predictive analytics. Using predictive analytics, financial organizations can forecast market movements and customer behaviors to optimize their investment strategies and risk management measures. In the context of corporate actions, this translates to the ability to anticipate events before they are formally announced, enabling proactive preparation and resource allocation.

AI goes beyond crunching numbers to properly evaluating context, correlating external factors—such as regulatory changes, competitive moves, and macroeconomic shifts—with internal data to create a richer, more actionable forecast. This contextual intelligence enables financial institutions to anticipate potential corporate actions by analyzing a company’s financial health, regulatory filings, market conditions, and historical patterns.

Natural Language Processing and Data Extraction

The challenge of extracting meaningful information from unstructured corporate action announcements is being solved through advanced natural language processing capabilities. Natural language processing (NLP) algorithms can analyze customer inquiries and messages to extract valuable insights, enabling more personalized responses and improving customer service experiences.

When applied to corporate actions, NLP technology can parse complex legal documents, extract key dates and terms, identify eligible securities, and classify action types with remarkable accuracy. This capability directly addresses the challenge of manual interpretation that currently creates bottlenecks and errors in processing workflows.

Machine Learning for Continuous Improvement

Unlike traditional forecasting methods that rely solely on past performance, these AI models continuously learn from new data, enabling them to adapt to emerging trends and market shifts in real-time. This learning capability enables AI systems to become more accurate and efficient over time, resulting in a compound improvement effect that progressively enhances operational performance.

The continuous learning aspect is particularly valuable in corporate actions processing, where regulatory changes, new event types, and evolving market practices require constant adaptation. Machine learning systems can automatically adjust to new patterns without requiring extensive reprogramming or manual intervention.

Blockchain: The Foundation for Trust and Transparency

Smart Contracts: Automating Corporate Action Execution

Blockchain technology introduces a revolutionary concept to corporate actions through smart contracts—self-executing agreements with terms directly written into code. Smart contracts operate by following simple “if/when…then…” statements written into code on a blockchain, with a network of computers executing the actions when predetermined conditions are met and verified.

In corporate actions processing, smart contracts can automate the entire lifecycle of an event. When a dividend is declared, the smart contract can automatically calculate distributions, verify eligible shareholders, execute payments, and update records—all without manual intervention. Once a condition is met, the contract is executed immediately, and because smart contracts are digital and automated, there’s no paperwork to process and no time spent reconciling errors that often result from manually completing documents.

Immutable Audit Trails and Compliance

Because there’s no third party involved, and because encrypted records of transactions are shared across participants, there’s no need to question whether information has been altered for personal benefit. This immutability creates unprecedented transparency and accountability in the processing of corporate actions processing.

The compliance benefits are substantial. Every action, from initial announcement to final settlement, is recorded on an immutable ledger that provides complete audit trails. Regulatory reporting becomes automated and verifiable, reducing compliance costs while enhancing regulatory confidence.

Distributed Ledger Technology for Real-Time Reconciliation

The entering of transaction data separately in each layer of the custody chain, thereby requiring costly reconciliation processes, would no longer be necessary in a distributed ledger system. This elimination of multi-layer reconciliation represents one of the most significant operational improvements possible in corporate actions processing.

The Distributed Ledger Technology (DLT) used in Blockchains has enabled the emergence of a new type of digital asset where the responsibility for updating the ledger was transferred to all or some of the holders of these digital assets. In corporate actions, this means that all participants—issuers, intermediaries, and investors—share a single source of truth that updates in real-time as events occur.

Enhanced Security and Risk Mitigation

Blockchain transaction records are encrypted, making them difficult to hack. Additionally, because each record is connected to the previous and subsequent records on a distributed ledger, hackers must alter the entire chain to change a single record. This security architecture addresses fundamental risks in corporate actions processing, where data integrity is paramount.

The decentralized nature of blockchain also eliminates single points of failure that plague traditional corporate actions systems. If one node experiences technical difficulties, the network continues to operate, ensuring business continuity even during system outages or cyber attacks.

Tokenization and Programmable Assets

Programmable money refers to digital currencies with conditions attached via smart contracts on blockchain platforms, where issuers define usage limits, designate recipients, set daily spending caps, and establish expiration dates. This concept extends to securities tokenization, where corporate actions can be embedded directly into digital assets.

Tokenized securities can automatically execute corporate actions without external intervention. A tokenized stock that splits 2-for-1 can automatically double the token holdings of every investor at the precise moment the action becomes effective. Dividend payments can be programmed to distribute automatically to token holders based on predetermined criteria.

The Convergence: AI and Blockchain Working Together

Creating the “Golden Record” Standard

“Turning various pieces of disconnected corporate actions data into unified ‘golden records’ that can then be relied on by hundreds of market participants as a definitive, single source of truth is truly a huge step forward,” according to Chainlink co-founder Sergey Nazarov. “This will help financial markets synchronize faster, reduce errors, and cut costs.”

The convergence of AI and blockchain creates the possibility of a truly unified corporate actions ecosystem. AI systems extract and standardize data from multiple sources, while blockchain technology ensures that this standardized data is immediately shared across all participants as an immutable, trusted record.

Oracle Networks: Bridging Digital and Physical Worlds

Chainlink connected its decentralized oracles with large language models (LLMs) to extract corporate actions data from various sources and transform it into a structured format. Oracle networks serve as critical infrastructure that brings external data into blockchain systems while maintaining security and decentralization.

In corporate actions, oracles can monitor multiple information sources—such as regulatory filings, company announcements, and market data feeds—and automatically trigger smart contract execution when specific events are detected. This creates a fully automated system that responds to events without manual intervention.

Automated Decision-Making and Execution

AI is integral to the execution and guidance of programmable money transactions, with AI models analyzing vast amounts of data, including market sentiment, price trends, and global events, to make informed trading decisions. When applied to corporate actions, this intelligence enables systems that not only process events but also optimize outcomes.

For example, AI can analyze market conditions to determine the optimal timing for voluntary corporate actions, while smart contracts ensure the automatic execution of these actions when the conditions are met. The system can simultaneously consider regulatory requirements, tax implications, market liquidity, and participant preferences to maximize value for all stakeholders.

Real-Time Processing and Settlement

The combination of AI and blockchain enables near-instantaneous corporate actions. Although proponents of the DLT technology are of the opinion that it should be possible to move from T+2 to T+0, even without the usage of smart contracts, the processing of corporate actions would be simplified in a DLT system.

AI systems can process and validate corporate action data in milliseconds, while blockchain infrastructure can execute and settle the resulting transactions immediately. This speed transformation turns corporate actions from multi-day processes into real-time events, dramatically improving capital efficiency and reducing operational risks.

Strategic Implementation: Building the Future-Ready Corporate Actions Platform

Technology Architecture, and Infrastructure Requirements

The successful implementation of AI and blockchain in corporate actions requires careful attention to infrastructure design. ML systems require substantial computing resources to process large datasets or streams of real-time financial data for predictive analytics model training, and the same applies to processing the trained model with real data for financial forecasting.

Organizations must strike a balance between performance requirements and cost considerations. Consider using cloud-based ML services, such as Amazon SageMaker or Azure Machine Learning, to access scalable computing resources, as well as out-of-the-box algorithms and pre-trained AI models. This approach allows firms to leverage advanced capabilities without massive upfront infrastructure investments.

Data Integration and Quality Management

The best way to achieve end-to-end reconciliation is to consolidate financial transaction data in one place, providing your team with visibility into every transactional activity within your business. For corporate actions, this entails developing comprehensive data integration strategies that connect traditional systems with emerging AI and blockchain platforms.

Data quality becomes even more critical in automated systems. Use obfuscated data, namely data sets anonymized via data masking techniques, to train your model. Ensure that you design and utilize your predictive analytics solution in full compliance with applicable data management and security regulations, such as the GDPR and PCI-DSS.

Regulatory Compliance and Risk Management

Financial companies operate in a highly regulated market with strict data protection standards and legislation, and financial data is sensitive information that can easily become a target for fraudsters and cybercriminals. Implementation strategies must prioritize regulatory compliance from the outset rather than attempting to retrofit compliance capabilities.

Rigorous assessment and validation of AI risk management practices and controls will become non-negotiable, as stakeholders will demand confidence in AI practices just as they do in other decision-critical information, such as financial results. This requirement extends to blockchain implementations, where regulatory clarity continues to evolve.

Change Management and Organizational Transformation

Foster a culture of innovation by encouraging a mindset that embraces change and continuous improvement. When your team is open to new ideas, it’s easier to integrate advanced technologies into everyday processes. The transformation of corporate actions processing requires significant organizational change management.

Corporate actions require specialized knowledge, which is becoming ever scarcer. When a financial institution automates its processes, the corresponding corporate actions know-how gets integrated into the programmed rules. This knowledge preservation becomes a strategic advantage as experienced professionals retire and new talent may lack specialized corporate actions expertise.

Phased Implementation Strategy

Organizations should approach AI and blockchain integration through carefully planned phases. Initial implementations might focus on specific corporate action types or market segments where benefits are most evident and risks are manageable. Single-event processing leveraging advanced platforms can help achieve better client servicing and reduced operational costs easily.

The phased approach enables organizations to develop expertise, demonstrate value, and refine processes before expanding into more complex scenarios. Each phase should deliver measurable improvements in processing speed, accuracy, or cost reduction while building capabilities for subsequent phases.

Industry Transformation: Case Studies and Results

BetaNXT: Comprehensive Platform Integration

BetaNXT’s DataXChange platform, built on Snowflake’s distributed cloud data platform, addresses these challenges by providing easier integration across systems, time-saving automation, and accelerated innovation. The platform demonstrates how comprehensive technology integration can address multiple corporate actions challenges simultaneously.

The measurable results include automated error reduction in voluntary corporate actions processing and real-time optimization of tax implications. Their AI analytics can analyze historical cost basis data to identify clients exhibiting behaviors that suggest they might leave the platform, providing 30-day, 60, and 90-day client retention warnings.

Chainlink-Euroclear Initiative: Industry Collaboration

The initiative’s first phase focused on corporate actions data for equity and fixed-income securities across six European countries, successfully demonstrating how AI, oracles, and blockchains can solve a decades-long challenge in unstructured data finance. This collaboration between technology providers and traditional financial infrastructure demonstrates the viability of large-scale transformation.

“By leveraging AI and Chainlink oracles to interpret, standardize, and deliver high-value unstructured data, we can dramatically reduce the manual processes required, enabling significant potential operational efficiency and cost reduction,” said Mark Garabedian, Wellington Management’s director of digital assets and tokenization strategy.

TCS BaNCS: Single Event Processing

TCS’s unique proposition of single-event processing, leveraging TCS BaNCS for Corporate Actions, can help achieve better client servicing and reduced operational costs with ease. The solution addresses the inefficiency of multiple entities processing the same corporate action event separately.

The application enables central operations and a center of excellence for managing asset servicing operations at scale globally, achieving lower processing costs per event of corporate actions by reducing the number of operations’ FTE. This demonstrates how technology can consolidate operations while improving service quality.

Lumen: AI-Powered Sales and Operations

While not specific to corporate actions, Lumen’s implementation demonstrates the transformative potential of AI in financial operations. Lumen uses Microsoft Copilot to summarize past sales interactions, as well as generate recent news, business challenges, broader industry trends, insights, and recommendations for next steps. That process traditionally took up to four hours per seller. In 2024, Lumen cut that time down to just 15 minutes and projects an annual time savings worth $50 million.

This case study illustrates the magnitude of efficiency gains possible when AI is properly integrated into operational workflows. Similar improvements are achievable in corporate actions processing, where manual research and analysis currently consume significant resources.

Risk Management and Mitigation Strategies

Technology Risk Considerations

Data Integrity: Automating data processing with smart contracts increases the risk of errors due to faulty data input. Ensuring data integrity is crucial to prevent inaccuracies and maintain reliable outcomes. Organizations must implement robust data validation and quality assurance processes.

The model can perform poorly when overtrained on a specific dataset (overfitting), and its performance can degrade over time due to progressive changes in input variables (model drift). Continuous monitoring and retraining protocols are crucial for maintaining the performance of AI systems.

Security and Cybersecurity Measures

Logic Hacks: Smart contracts can be exploited if they contain poorly coded logic, compromising the entire blockchain ecosystem. Regular audits and secure coding practices are essential. Security considerations must be integrated into every aspect of system design and implementation.

This includes protecting the solution with security measures like data exchange encryption, identity and access management, and multi-factor authentication. Comprehensive security frameworks must address both traditional cybersecurity threats and new risks introduced by AI and blockchain technologies.

Regulatory and Compliance Risks

Compliance: The lack of comprehensive regulation for smart contracts and blockchain technology exposes organizations to scrutiny. Establishing robust corporate compliance policies can mitigate risks related to blockchain attacks and human error.

Organizations must proactively engage with regulators and industry bodies to ensure their implementations align with evolving regulatory expectations. This includes maintaining flexibility to adapt systems as regulatory frameworks develop.

Operational Risk Management

Public blockchain ledgers enable secure data processing but make collected data publicly accessible, raising privacy concerns. Private blockchains can enhance privacy through encryption and controlled access, but they limit the data volume accessible for AI, potentially affecting the accuracy of AI decision-making and analytics.

Careful consideration of blockchain architecture choices is essential. Organizations must balance transparency benefits with privacy requirements and operational constraints.

Future Outlook: The Next Decade of Corporate Actions

Market Transformation Trajectory

In 2025, a smaller group of industry leaders will begin to pull ahead of their peers. Those companies with higher quality data and more standard processes will use AI to improve efficiency and insights, accelerate R&D, and slash go-to-market time. This competitive separation will be particularly pronounced in corporate actions processing, where automation advantages compound over time.

The use of AI in 2025 should be accelerated by a more flexible regulatory environment, with the new administration likely to shift oversight in this sector toward self-governance, creating more space for innovation. This regulatory evolution could accelerate the adoption of AI and blockchain solutions in financial services.

Technology Evolution and Convergence

The global blockchain AI market size was USD 445.41 million in 2023, calculated at USD 550.70 million in 2024, and is expected to reach around USD 3,718.34 million by 2033, expanding at a solid CAGR of 23.64% over the forecast period. This explosive growth indicates sustained investment and development in convergent technologies.

AI and blockchain integration promise enhanced security and efficiency. Future projects include precision farming, unmanned exploration, and AI-powered metaverse experiences, indicating transformative shifts in technology. Corporate actions processing will benefit from advances developed across multiple industries and use cases.

Emerging Use Cases and Applications

Microinsurance policies are just one example of how the cost advantages of stablecoins and insights from AI are helping to create new, more intelligent markets. Similar innovations will emerge in corporate actions, creating new products and services that were previously impossible or uneconomical.

Apart from data on transactions, distributed ledgers could also contain computer code, so-called ‘smart contracts’, which could be used to automate certain non-elective corporate actions. The expansion from voluntary to mandatory corporate actions automation represents a significant evolution in market infrastructure.

Industry Standardization and Interoperability

Standardizing the Corporate Action lifecycle can reduce the effects that fragmentation currently has on the industry, especially if there is standardization on both ends of this lifecycle. The combination of AI and blockchain technologies creates opportunities for industry-wide standardization that has been elusive with traditional technologies.

Future developments will likely focus on creating interoperable standards that allow different AI and blockchain systems to work together seamlessly. This interoperability will be essential for realizing the full benefits of technology transformation across the entire corporate actions ecosystem.

Strategic Recommendations and Conclusion

Immediate Action Items for Financial Services Leaders

Financial services executives must recognize that corporate actions transformation is not a future consideration—it is a present competitive necessity. Organizations should immediately assess their current corporate actions processing capabilities and develop comprehensive modernization strategies that integrate AI and blockchain technologies.

The first priority should be establishing data quality and integration capabilities. The key goal of a data lineage tool is data lifecycle management, right from the data origination to the data exhaustion. Without high-quality, well-integrated data, both AI and blockchain implementations will fail to deliver expected benefits.

Investment Priorities and Resource Allocation

Your company’s AI success will be as much about vision as adoption. Your AI choices may be the most crucial decisions not just this year but of your career. This observation applies directly to corporate actions transformation, where early investments in the right technologies and partnerships will determine long-term competitive positioning.

Organizations should prioritize investments in platforms that combine AI and blockchain capabilities rather than implementing these technologies separately. The convergence benefits are too significant to ignore, and integrated platforms will deliver superior results compared to point solutions.

Building Strategic Partnerships

The complexity of AI and blockchain implementation makes strategic partnerships essential. Participants in the initiative include Euroclear, Swift, UBS, Franklin Templeton, Wellington Management, CACEIS, Vontobel, and Sygnum Bank, with blockchain ecosystem partners Avalanche, ZKsync, and Hyperledger Besu networks also contributing.

These collaborations demonstrate that successful transformation requires cooperation between traditional financial institutions, technology providers, and blockchain infrastructure companies. Organizations should actively participate in industry initiatives and consider strategic partnerships that accelerate their transformation timelines.

The Imperative for Action

The transformation of corporate actions processing through AI and blockchain represents one of the most significant operational improvements available to financial services firms. The benefits—dramatic cost reduction, elimination of manual errors, real-time processing, and enhanced transparency—are too substantial to ignore.

Automating and standardizing this information could help significantly reduce operational inefficiencies that currently cost businesses millions of dollars every year due to errors and manual data processing. Organizations that delay implementation will find themselves increasingly disadvantaged as early adopters realize compound benefits from their technology investments.

The future of corporate actions is not just faster—it is smarter, more transparent, and fundamentally more efficient. Financial services leaders who embrace this transformation today will define the competitive landscape for the next decade. Those who hesitate will find themselves struggling to catch up in an increasingly automated and intelligent market environment.

Innovation in corporate actions processing is no longer optional—it is imperative for survival and success in the digital financial services era.