Tackling Corporate Actions Data Conflicts: An AI-based Recommendation Engine

Corporate actions data – such as mergers, acquisitions, dividends, and stock splits – plays an important role in helping financial institutions and their clients provide accurate financial reporting on cash and security proceeds, and in making decisions where these alternatives are offered by issuers.

However, firms receive this data from a variety of sources, each providing their own interpretation of issuer announcements. This leads to inconsistencies in details, such as dates, rates, prices, taxation, options, and payouts. These discrepancies lead to data conflicts in the processing applications that can disrupt operations, processing, and reporting, especially for large global players handling hundreds of data sources and millions of data conflicts annually.

Addressing the Limitations of Manual Processing

Traditionally, resolving data conflicts require meticulous manual intervention by operations teams. During peak seasons, such as quarterly or annual earnings reports, or significant corporate restructuring periods, the volume of incoming data can overwhelm even the most experienced teams. Based on data validation by S&P Global Market Intelligence (“Market Intelligence”), it is estimated that approximately 70% of data conflict resolutions require operations users to consult unstructured data sources, such as websites or publications, which is time-consuming and error prone.

To tackle these challenges, Market Intelligence’s Corporate Actions solution offers an innovative recommendation engine leveraging AI-based technology. This system employs gradient-boosting algorithms to enhance the resolution process of corporate actions’ data conflicts.

Understanding Gradient Boosting

Gradient boosting is a machine-learning technique that builds predictive models iteratively. It corrects the errors of previous models by combining them and using a weighted sum of predictions from all models in the ensemble. This approach continues to add models until a specified number is reached or the performance stops improving significantly.

With the Corporate Actions solution, the gradient boosting approach helps to sift through historical data on conflict resolutions and identifies patterns and insights that can guide current decision-making.

How the Recommendation Engine Works

The recommendation engine for data conflicts resolution analyzes a vast array of historical data on resolved conflicts, learning from past discrepancies and resolutions. By applying gradient boosting, it generates recommendations for handling current data conflicts. The engine evaluates different data elements, such as dates, rates, and options, against established patterns and provides suggestions on how to address the inconsistencies. These recommendations are then made available to operations users who can either apply or reject them, thereby providing additional data for model improvement.

Benefits of the AI-Based Recommendation Engine

By utilizing historical data and sophisticated algorithms, the recommendation engine significantly reduces the risk of human error. It offers a data-driven approach to resolving conflicts, improving the accuracy of the corporate actions data. Moreover, once the engine’s recommendations are found to be accurate, they can be used to configure rules in the core processing applications, automating conflict resolution for underlying parameters. This efficiency gain is especially valuable during peak seasons when the operational workload spikes.

Scalability and Adaptability

As the volume of corporate actions data continues to grow, the recommendation engine scales seamlessly. Its AI-driven nature ensures that it adapts to new patterns and emerging data trends, maintaining its effectiveness even as data complexity increases.

Transforming Corporate Actions Data Management

The integration of AI in managing corporate actions data is not just a technological upgrade, it is a transformative shift that addresses the core challenges faced by financial institutions today. By providing data-driven recommendations, Market Intelligence's AI-based recommendation engine for data conflicts resolution significantly enhances accuracy, efficiency, and reliability. This innovative solution supports operations teams, enabling them to navigate the complexities of corporate actions data with greater ease and confidence. As the financial industry continues to evolve, embracing such advancements will be key to maintaining robust operations and staying ahead in an increasingly data-driven world.