AI's Slow Burn ⏳🔥

Hi, I'm Matt. Welcome back to AI Street, your weekly brief on AI + finance. Here’s the latest:

The Rundown 

• AI’s (Non?) Adoption

• Milan Fintech Summit

• 5 Minutes with Sov.ai’s Derek Snow

• Fundraising: Quartr Secures $6M

• Regulation: Open (Source) AI

• Research: AI’s VC picks

• Roundup

BIG PICTURE
Success Theatre

While many financial services firms tout AI's benefits for boosting productivity and efficiency, tangible results are often lacking. As HSBC's head of generative AI, Edward Achtner, noted at a recent tech event, "Candidly, there's a lot of success theater out there," according to CNBC.

This sentiment has been echoed recently:

• Man AHL CIO suggests AI's "game changer" effect is overstated, predicting a 'slow' evolution rather than revolution. (Investment Week)

• A St. Louis Fed study indicates shifts in corporate investment post-ChatGPT may be more hype than reality. (St. Louis Fed)

AI's impact is real, but it's not happening at breakneck speed. A lot of firms are still a "deer in the headlights" – recognizing AI's potential but unsure how to proceed effectively, as Sov.ai’s Derek Snow says in the interview below.

MILAN FINTECH SUMMIT

I moderated a panel on AI this week at the Milan Fintech Summit, which was a great event.

I learned a lot about “embedded finance.” (To be honest, I didn’t know much about it until this week.)

Turns out, I already use it. And you probably do too. Apple Pay is an example of embedded finance. And it’s come in handy recently.

Earlier this month, I got a warning of a fraudulent transaction on my credit card. I called Chase to confirm that it wasn’t me booking a hotel in Oregon. So they sent off a new credit card. But my Apple Pay was updated instantly. So I could use the new card immediately.

For that to happen, there’s lots of coordination behind the scenes among different companies, says Paolo Zaccardi, CEO and Co-Founder of Fabrick, whose subsidiary Fintech District co-organizes the Milan Fintech Summit.

And AI is likely to take that a step further.

“AI is transforming financial services by combining multiple backend processes into seamless customer experiences,” Zaccardi told me. “We're moving towards a future where AI agents can understand and execute complex financial tasks without the user even realizing it.”

INTERVIEW
Five Minutes with Sov.ai’s Derek Snow

AI is writing novels, coding apps, and even passing bar exams—𝙗𝙪𝙩 according to Dr. Derek Snow, founder of Sov.ai, we’re 𝙨𝙩𝙞𝙡𝙡 in the early stages.

Agents (algorithms that perform tasks) + Multimodal AI (systems that handle text, audio, and images) are setting the stage for a major shift in finance.

“When we combine these agent-based, multimodal LLMs with the vast amount of financial data available, we’ll see truly transformative applications in finance. But we’re not quite there yet.”

In this week’s “5 Minutes with” interview, Derek breaks down the future of AI-driven financial analysis, the potential of agent-based systems in trading, and the challenges of integrating AI with existing financial infrastructure.

Derek’s background

• Ph.D. in finance from the University of Auckland 

• Visiting doctoral scholar at NYU's School of Engineering and Cambridge Judge Business School 

• Alumnus of:  

  • The Alan Turing Institute

  • The UK's Institute for AI

  • The Oxford-Man Institute of Quantitative Finance at Oxford University 

• Currently teaching machine learning in finance at NYU

• Author of MLQuant newsletter 

Derek is also the founder of Sov.ai, a quant research platform for fundamental investors. Sov.ai provides investors with datasets and tools that can spot trends and patterns in financial markets, without requiring them to be expert programmers or data scientists themselves.

Derek, who’s a native of New Zealand, splits his time between NYC and Boston.

Key Takeaways

1. AI vs. Automation: Much of what is called “AI” in business is actually automation.

2. Slow AI Adoption in Finance: Despite the excitement around AI, its adoption in finance remains slow, especially at larger institutions due to data quality and regulatory challenges.

3. Promising AI Applications: AI shows promise in areas like risk management, algorithmic trading, credit risk assessment, and natural language processing for financial analysis.

4. AI Risks: Over-reliance on AI, regulatory scrutiny, and the potential for bias are key risks that financial professionals should remain cautious about.

5. Sov.ai’s Role: Sov.ai aims to make financial data more accessible for AI applications, offering pre-processed datasets that are easier for firms to use.

6. Future AI Trends: Agent-based language models and multimodal AI could significantly enhance financial analysis.

This interview has been edited for clarity and length.

How is AI adoption progressing in the finance industry?

AI adoption in finance is still relatively slow, especially at larger institutions. Many firms are using AI for basic tasks like code generation, but more sophisticated implementations are limited. There's a lot of marketing hype around AI usage, but the reality often falls short.

Some prop trading firms and quantitative hedge funds are more advanced in their AI adoption, particularly those with a mandate for applying quantitative methods. However, even at firms actively hiring for AI-related roles, new employees often find limited opportunities to experiment or implement cutting-edge techniques.

The median age of portfolio managers, especially in the US, tends to be around 55, which may contribute to slower adoption. Additionally, regulatory requirements can limit the use of advanced AI models in certain areas like options pricing or credit decision-making.

The rapid pace of AI development makes it hard to decide when and how to invest in new technologies. Data quality and availability issues, especially for training sophisticated AI models, pose additional challenges. Compliance hurdles in adopting new technologies or data sources and the need for specialized talent to develop and implement AI solutions further complicate matters.

Many firms are in a "deer in the headlights" moment, recognizing AI's potential but unsure how to proceed effectively.

…

What developments in AI are you watching for in the context of LLMs and finance?

First, I'm watching for advancements in agent-based language models. These could significantly enhance how AI interacts with financial systems and data.

Second, I'm keen to see progress in incorporating multimodal data into LLMs. Currently, LLMs don't really handle things like Excel documents or complex financial reports well. Once they can effectively process and analyze these types of structured financial data alongside text, we'll see a step change in their capabilities for financial analysis and decision-making.

When we combine these agent-based, multimodal LLMs with the vast amount of financial data out there, that's when we'll likely see truly transformative applications in finance. But we're not quite there yet.

Read the full interview here 

ICYMI
The last 5 Five Minute Interviews

• Bain’s Richard Lichtenstein on AI adoption in private equity

• Snowflake’s Jonathan Regenstein on AI building novel datasets

• Skadden’s Dan Michael on the SEC’s AI stance

• Stardog’s Matt Lucas on hallucination-Free AI

• Celent’s Monica Summerville on AI adoption in capital markets

COVERAGE

FUNDRAISING

Quartr Secures $6M for AI-Powered Research

Stockholm-based Quartr, a provider of AI-driven financial research tools, has raised $6 million from Altos Ventures, which will also take a board seat.

• Quartr, which launched in 2020, has gained traction with major hedge funds, asset managers, and Fortune 500 companies, according to a company press release.

• The company provides information on 10,000+ public companies to 8 million+ users.

REGULATION
Banking Group Drafts AI Guidelines for Finance Sector

The Fintech Open Source Foundation (FINOS), a banking and technology industry group, has released draft guidelines for adopting AI in the financial sector.

FINOS aims to establish industry-wide standards for AI use, similar to its previous work on cloud computing standards.

The organization plans to release open-source code for testing these controls in the coming months, marking a step towards responsible AI adoption in the financial sector. (Banking Dive)

Also:

• OCC Solicits Research on AI in Banking and Finance

• Decoding California’s Recent Flurry of AI Laws 

RESEARCH

AI Boosts VC Decision-Making: Study

A new study demonstrates how large language models can enhance venture capital decision-making by leveraging textual data from startup profiles.

Researchers developed a "fused" large language model that combines structured data (like founder info and funding history) with how startups described themselves in writing.

• They tested this program using information from over 20,000 real startups from Crunchbase.

• Their AI was able to predict which startups would be successful about 74% of the time.

• Textual descriptions were found to be the single most important feature for predictions, outweighing all structured data inputs.

Paper: A Fused Large Language Model for Predicting Startup Success
Authors: Abdurahman Maarouf, Stefan Feuerriegel, Nicolas Pröllochs

Hat tip to Zanista.AI, which uses AI to scan relevant research papers, for alerting me to this.

AI RESEARCH

With 3Q earnings season kicking off this week, I’m looking for AI-driven research to highlight. Reach out if you have unique insights on specific companies by leveraging AI. [email protected]

ROUNDUP

A Godfather of AI Just Won a Nobel. He Has Been Warning the Machines Could Take Over the World.

• Geoffrey Hinton hopes the prize will add credibility to his claims about the dangers of AI technology he pioneered. (WSJ)

JPMorgan Patent Could Add Guardrails to AI in Banking

• Given that AI systems can’t always be totally accurate, observing when they make mistakes could mitigate a lot of harm. (Daily Upside)

AI Startup for Personal Injury Law Valued at Over $1 Billion

• EvenUp Inc., a personal injury startup making AI products for law firms, has raised $135 million in a new funding round valuing it at more than $1 billion. (Bloomberg)

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