The emergence of innovative Artificial Intelligence technologies such as Open AI’s ChatGPT and Google’s Bard has made recent headlines as we begin to think about what these new developments in AI may mean for the nature of work and entire industries.
In Private Capital Markets, AI and Machine Learning have been used for some time, but what makes the progress of the past few months interesting is the emergence of Generative AI. Generative AI is a broad term that encompasses any AI system that can create new text, data, code and imagery. This is a significant change from the current use of AI in markets where algorithms and smart searches are used to assess, organize and return existing data and content for issuers, brokers and dealers.
In addition to the ability to generate data and content, these systems use GPT-3 (Generative Pretrained Transformer), a powerful language model developed by OpenAI, capable of generating human-like text allowing it to complete tasks such as translation, summarization as well as computer-orientated tasks such as software coding.
At Katipult, our engineering and development teams use AI tools in building our innovative DealFlow products.
For example, AI generates code that our developers use as resources for planning and optimal algorithms as they write code. The AI then follows up by writing code that tests our software, speeding up the development of high-quality, robust lines of code.
We also leverage AI’s ability to analyze database structures to identify efficiencies in the data model, which provides insights to our engineering team to enhance our technology further.
Finally, we harness the power of these tools to assist with code review and fixing bugs.
All these steps, crucial in software development, are made quicker and more efficient now that Artificial Intelligence is at a point where contextual factors around a question are considered when processing outputs.
This makes the job of being a Katipult developer more exciting and boosts productivity by 20 to 30%, and allows our teams to focus on higher-value engineering work. This results in a better and more stable software product as our teams can focus their energies on the more critical tasks associated with building our products.
AI and Private Capital Markets
We have a similar perspective on how this technology could impact the broader world of Private Capital Markets.
AI will likely offer a significant advantage by doing task-orientated work quicker and more efficiently, enabling humans to focus on making more nuanced decisions, often of higher value.
As the adoption of tools becomes widespread, more interesting use cases will come when AI can assist with more abstract problems, such as due diligence or comparison of deal opportunities.
For example, screening Private Placements for high-quality versus low-quality deals requires investors to assimilate a lot of information, evaluate it and then make judgements to identify the best deals. While humans will still need to determine the best investments to make for an individual, using AI will enable investors to make decisions more efficiently.
For Equity Capital Market teams who do a lot of work around due diligence, these tools could be used to pass data and information to AI and then ask it to identify;
- Are there whole datasets or information that need to be included?
- Which areas still need more information?
- How much due diligence is complete?
By doing this, teams can focus on the parts of the problem where information is lacking and save time by not having to focus on the completed areas.
While today’s AI is changing how constrained applications such as software development and content creation are approached, the future is even more exciting. As AI advances to compute abstract concepts, and when there is broader access to Private Placements, these tools may herald a new age of Private Capital Markets.
