How AI is transforming asset servicing

The corporate actions landscape is experiencing a surge of complex events and is challenging traditional processing techniques. Operations teams continue to struggle with managing intricate workflows and a flood of unpredictable information, exacerbated by convoluted processes and fragmented data sources.

In today’s rapidly evolving financial landscape, the integration of artificial intelligence (AI) is revolutionising how organisations operate, analyse data, and make decisions. One area where AI is making significant strides is in corporate action processing—a critical component of post-trade operations.  The adoption of AI in post-trade processing represents a significant opportunity for improvement.  

In the first of a four-part series we will explore the transformative impact of AI on post-trade corporate actions. We will delve into various use cases, showcasing how our full end-to-end workflow solutions can relieve operations teams from day-to-day management, allowing them to focus on core business activities. Each blog will highlight different aspects of AI in post-trade processing, from data conflict resolution to workflow orchestration, and provide actionable insights to help you navigate this complex landscape.

Series 1: Understanding corporate actions and data conflicts

Corporate actions are events initiated by a company that affect its securities. These actions can have significant implications for investors and require precise handling to ensure that all stakeholders receive the correct information. However, the complexity of corporate actions often leads to data conflicts—discrepancies between various data sources regarding the details of the action.

Financial institutions depend on numerous sources, each offering distinct interpretations of issuer announcements. This results in inconsistencies in critical details such as dates, rates, prices, taxation, options, and payouts. The impact of untimely notifications significantly compounds these discrepancies, particularly in corporate action processing. Delayed or inaccurate information can create data conflicts within processing applications, leading to disruptions in operations and reporting. This challenge is especially pronounced for large global players that manage hundreds of data sources and confront millions of data conflicts each year, highlighting the necessity for timely and precise notifications to ensure efficient corporate action processing.

Traditionally, resolving data conflicts necessitates careful manual intervention by operations teams. However, during peak seasons—such as quarterly or annual earnings reports or significant corporate restructuring events—the sheer volume of incoming data can overwhelm even the most seasoned professionals. The complex nature of these events often leads to delays, as human error can inadvertently occur during the resolution process.

Based on our data validation experience, it is estimated that around 70% of data conflict resolutions require operations users to consult unstructured data sources, such as websites or publications. This reliance on external sources not only proves to be time-consuming but also increases the likelihood of errors, further complicating the resolution of data conflicts.

The role of AI in resolving data conflicts

AI technologies, particularly machine learning and natural language processing, offer innovative solutions to tackle the challenges associated with corporate actions data conflicts.

The recommendation engine operates by analysing a vast array of historical data related to previously resolved conflicts. The system learns from past discrepancies and resolutions by employing sophisticated algorithms, particularly gradient boosting techniques, allowing it to generate informed recommendations for current data conflicts. This approach significantly enhances the efficiency of the resolution process, as it evaluates various data elements—such as dates, rates, and payouts—against established patterns derived from historical data.

When a data conflict arises, the recommendation engine assesses the inconsistencies by comparing the conflicting information to the patterns it has learned. It then produces tailored recommendations for operations users, suggesting the most likely correct values based on past resolutions. This data-driven approach reduces the reliance on manual intervention, which can be error-prone and time-consuming. Instead of sifting through unstructured data sources, such as websites and publications, operations teams can leverage the engine’s insights to make informed decisions more swiftly.

Moreover, the recommendation engine not only aids in resolving current conflicts but also continuously improves its accuracy over time. As operations users apply or reject the recommendations, the system gathers feedback, allowing it to refine its algorithms and enhance its predictive capabilities. This iterative learning process ensures that the engine becomes increasingly adept at identifying and resolving data conflicts, ultimately leading to better accuracy in corporate actions data.

The impact of this technology is particularly pronounced during peak seasons, such as quarterly earnings reports or significant corporate restructuring periods, when the volume of incoming data can overwhelm even the most experienced teams. By automating aspects of the conflict resolution process, S&P Global Market Intelligence’s recommendation engine not only improves operational efficiency but also helps mitigate the risks associated with human error.

S&P Global Market Intelligence’s data conflict resolution recommendation engine represents a significant advancement in the management of corporate actions data. By harnessing AI and historical data analysis, the engine provides timely and accurate recommendations, ultimately enhancing the reliability of financial information for clients around the globe. This innovative approach not only streamlines operations but also reinforces S&P Global Market Intelligence’s commitment to delivering high-quality data solutions in an increasingly complex financial landscape.

How S&P Global Market Intelligence can help

At S&P Global Market Intelligence, we are at the forefront of this transformative wave, leveraging AI technologies to enhance our services and deliver unparalleled insights to our clients.

We are revolutionising this landscape by staying at the forefront of technological innovation. Using an iterative, data-driven approach and real-world feedback, our advanced AI tools refine data analysis, extract deeper insights, and drive informed decisions, allowing for more efficient, transparent, and seamless execution of corporate actions.

As we continue to innovate and adapt to the changing needs of the market, we remain dedicated to providing cutting-edge solutions that empower businesses and investors alike, ensuring they stay ahead in an increasingly data-driven world.