SCRIBESTAR, AI AND THE USE OF STRUCTURED DATA FROM PROSPECTUSES AND OTHER OFFERING DOCUMENTS
Relevance and opportunities for issuers, investors, exchanges, regulators and other market participants
Prospectuses and other offering documents contain important information about a company’s financial performance, business operations, future prospects and the securities its placing in the market. This data is typically provided to investors when a company issues new securities, such as stocks or bonds, through an initial public offering (IPO) or secondary offering.
Traditionally, investors have relied on financial analysts and advisors to interpret and analyse this data to make informed investment decisions. However, with the increasing availability and accessibility of artificial intelligence (AI) technologies, there is a growing possibility for analysts, advisers but also investors directly to utilize AI to quickly extract valuable information from prospectuses and streamline the investment decision process.
Opportunities and benefits of using AI in prospectus analysis
It is generally more efficient and effective to use AI to analyse data from offering and listing documents if the data is in a machine-readable format, such as JSON or XML, rather than a traditional format like PDF or Word. This is because machine readable formats are structured in a way that is more easily recognizable and interpretable by computers and algorithms, whereas traditional formats are typically designed for human readability.
For example, machine readable formats like JSON or XML typically use tags or other markers to identify specific pieces of information and the relationships between them, allowing AI algorithms to extract and analyse specific data points or groups of data more easily.
Additionally, machine readable formats are often more flexible and easier to manipulate, allowing AI to extract and filter specific pieces of data.
One way in which AI can be used to extract valuable information from offering documents is through natural language processing (NLP) techniques. NLP involves using algorithms to analyse and understand human language, including the meaning and context of words and phrases. By applying NLP to offering documents, AI can extract and contextualise key information such as financial performance metrics, business strategies, and risk factors, and present it in a format that is easily understood by investors.
Another potential use of AI in prospectus analysis is through the use of machine learning algorithms. These algorithms can be trained to identify patterns and trends in the data contained in offering documents, such as changes in a company’s financial performance over time or correlations between multiple companies or between certain business strategies and financial outcomes. This can allow investors to identify potential investment opportunities or risks that may not be immediately apparent from a cursory review of the data.
Additionally, by automating some of the data analysis process, AI can help reduce the risk of human error or bias, resulting in more reliable and objective analysis.
Limitations and considerations
However, it is important to note that AI is not a replacement for human expertise and judgment. While AI can provide valuable insights and analysis, it is ultimately up to investors to make their own informed decisions about whether to invest in a particular company or security. Investors should seek out multiple sources of information and consider the limitations and potential biases of any analysis, including that produced by AI.
It is also worth noting that the use of AI to analyse prospectus data is not without its limitations. One potential limitation is that AI algorithms may not be able to fully understand or accurately interpret complex or nuanced language, particularly if it is used in a way that is not consistent with standard usage. This can lead to errors or misunderstandings in the analysis, which can in turn lead to incorrect or misleading conclusions.
Another potential limitation is that AI algorithms may be biased in their analysis if the data used to train them is biased. For example, if an AI algorithm is trained on a dataset that is heavily skewed towards a certain type of company or industry, it may be more prone to making biased predictions or recommendations when applied to other types of companies or industries. It is important for investors to be aware of this potential bias and to consider it when evaluating the results of AI-based analysis.
It is important to remember that AI is only as good as the data it is given. If the data contained in offering documents is incomplete, inaccurate, or misleading, the AI algorithms used to analyse it will also be flawed. It is therefore important for investors to carefully review the quality and reliability of the data and it is for the regulators and exchanges to make sure rules and regulations on the contents of offering documents are respected.
To make the most of the opportunities presented by AI in offering documentation analysis, it is important for the data to be in a machine-readable format, such as JSON or XML, that is easily interpretable by algorithms. While it may be possible to use AI to analyse traditional formats like PDF or Word, it may be more time-consuming and less accurate compared to using machine readable formats.
ScribeStar is streamlining the prospectus data for AI-driven analysis
ScribeStar is well positioned to help investors and other key stakeholders in the capital markets value chain take advantage of the opportunities presented by AI in prospectus and offering documents analysis. ScribeStar is a collaboration tool that is designed to help issuers and their deal-teams efficiently produce prospectuses and other offering documents in machine readable formats.
By providing a platform for streamlined document production, compliance and data creation, ScribeStar can help deal-teams ensure that data in offering documents is accurate, up-to-date, and in a format that is easily interpretable by AI algorithms.
This is not only relevant for the perspective of issuers and investors, but also for securities exchanges where these securities are placed and traded and the regulators.
The use of AI in offering documentation processing can improve the efficiency and accuracy of the process for reviewing new securities offerings. There are typically strict requirements for the information that must be included in prospectuses, and the process of reviewing and approving these documents can be time-consuming and resource intensive, again something that ScribeStar’s technology neatly solves.
Producing documents in machine-readable format, opens the opportunity to utilise AI to extract key information from prospectuses and identify potential risks, issues, or even errors, omissions or inconsistencies, allowing for a more efficient suitability evaluation.
Streamlined AI-driven data analysis of documents can also help securities exchanges gain access to information for their own data products as well as in the creation of new ways to better serve customers, issuers and market members.
Depending on market venues, issuers and their deal-teams are sometimes required to manually input certain information into the exchange’s systems, or more commonly this is done by the exchange. It can be a tedious process time-consuming that runs a risk of errors and omissions.
By producing documents in machine-readable format, such as it’s done by ScribeStar, data from documents can be output directly into systems on the exchange side, removing the afore mentioned risks, optimising business processes and generating cost-savings.
Conclusion and recommendations
The use of AI to analyse structured data from prospectuses has the potential to provide valuable insights and information to investors, and help exchanges and regulators streamline the flow of data and the issuance process.
The prerequisite for that is that documents are produced in machine-readable formats that allow a much easier and efficient AI deployment.
By optimising the documentation production process for issuers and their deal-teams and combining it with automated creation of machine-readable outputs, ScribeStar is a unique tool that enables those prerequisites whilst making the issuance process easier and more efficient for all the parties involved.
