Technology and Innovation Community

Good morning everyone!

Fantastic and encouraging to see so many new members joining - warm welcome! Feel free to connect with each other or with me.

While onboarding new members, I am hearin often that one of the important themes they'd like to learn more on is use cases of generative AIs in the investment / finance sphere. I found this article here and here's the brief review:

The article highlights how generative AI is transforming the financial sector by enhancing personalized financial advice, improving risk assessments, and streamlining customer interactions.

Examples include:

• automating the creation of investment portfolios
• analyzing large datasets for predictive insights
• providing customized recommendations based on user profiles. 

AI also supports fraud detection, regulatory compliance, and operational efficiency in areas like credit scoring and customer service chatbots. Despite these advancements, challenges such as data privacy, ethical considerations, and maintaining human oversight are critical to address. For details, see the article here.

Do we have members using AIs on a portfolio / organisation level? Would be great to hear from you on what you are finding most exciting or most challenging about it?

Here are the key takeaways from the EY article on "Harnessing the Power of AI to Augment Human Investment Decision-Making":

1. AI as an Augmenter, Not a Replacer: AI should be viewed as a tool that enhances human decision-making, rather than replacing it. It supports asset managers by analysing large datasets, recognizing trends, and providing predictive insights to make better-informed investment choices.
   
   
2. Risk Management: AI can help identify potential risks and provide real-time alerts, enabling asset managers to take preventative action and improve risk mitigation strategies.
   
   
3. Personalization of Client Solutions: AI allows for tailored investment advice by analysing individual client goals, preferences, and risk appetites. This customization enhances customer satisfaction and engagement.
   
   
4. Operational Efficiency: AI automates repetitive tasks like data collection, portfolio reporting, and compliance checks, freeing up resources for more strategic initiatives and improving efficiency.
   
   
5. Challenges and Considerations: While AI offers significant advantages, it also poses challenges, such as the integration of AI into existing workflows, data privacy concerns, and the need to navigate regulatory compliance.

For a deeper dive, you can read the full article here: EY: Harnessing the Power of AI.

Do we have members with experience in this? How has AI helped you so far? Or not?

Adding a use case: 

1. Categorizing assets. In the era of open banking and an increasing trend towards alternative assets, we are increasingly seeing challenges in consolidating assets across custodians and categorizing each asset correctly. A private debt strategy can easily be mistaken for a distressed credit hedge fund. Using AI and machine learning to better classify assets and in turn, better judge risk & return characteristics is an important investment-relevant use case.