AI is eating the world, or so the headlines say. Big tech plans to invest roughly $320 billion in AI this year and financial institutions are following suit.

But beyond the headlines, what does this really mean for debt markets? Where does AI truly excel, and what limitations persist? What implications does this technological shift hold for analysts' jobs? And how is 9fin deploying AI to address specific challenges confronting debt market professionals?

At a recent webinar Sujeet Indap, Wall Street Editor at the Financial Times, sat down with Steven Hunter, CEO and co-founder of 9fin, to cut through the hype and dissect the real impact of AI on debt markets.

Watch the recording below, or keep scrolling to catch up on the key takeaways.

The finance ‘apprenticeship’ model meets AI

Having both forged careers in investment banking — Steven at JP Morgan, Sujeet at Merrill Lynch — they opened the webinar by discussing how AI is reshaping early career training for analysts.

This model has remained largely unchanged for decades, with analysts developing expertise by manually spreading comps, analysing 10-Ks line by line, and building financial models from scratch — tedious but crucial work that cultivates pattern recognition skills and judgment.

The transmission of knowledge has followed an unbroken chain, with today's analysts learning as much as their predecessors did decades ago.

However, with the rise of large language models (LLMs) and AI-driven tools, many of the foundational tasks traditionally undertaken by analysts, like drafting memos and search and information retrieval from vast datasets, are becoming automated.

The emergence of AI, therefore, raises important questions about how financial professionals will develop expertise in the future. While some senior professionals believe juniors should "go through the same pain and suffering" they experienced, there's growing recognition that AI can free analysts to focus on higher-value activities earlier in their careers.

The industry now faces a significant challenge: how to maintain the deep financial understanding traditionally developed through manual work when AI increasingly handles these foundational tasks.

Pattern recognition vs. nuanced judgement

Steven was also keen not to overhype the capabilities of AI in finance, highlighting the distinction between routine pattern recognition and more nuanced judgment calls. Steven explained that "a lot of deal-making in our space is about relationships — having a good relationship with the private equity sponsor or the end client, and being able to walk them through how a transaction works."

While AI excels at identifying patterns, it is less capable of handling financial nuance and context, grasping, for instance, how working capital dynamics truly impact a company's operational reality.

This distinction reflects the current limitations of AI systems — they excel at routine tasks but struggle with the complex judgment calls that define high-level financial decision-making. Importantly, Steven emphasised that human skills are becoming more valuable than ever in this environment:

Rather than exhausting hours extracting data or reformatting information, people can begin with structured insights and pivot to more substantive questions: How should these numbers be forecast? What subtleties exist in the quality of earnings? What implications does this specific disclosure carry?

He believes this augmentation will ultimately elevate the work itself, freeing people up to focus on the more interesting, higher value work.

Real solutions vs. AI hype

So, how do firms cut through the AI hype? For Steven, the the most important thing is to focus on actual problems rather than adopting technology for its own sake:

"I've been on some client calls where they're using a generic AI tool. They often don’t know what problem it's solving or why they onboarded it. I’ve had people say, 'Well, we saw that it raised a lot of money, and some of our competitors are using it.' The best one I heard was, 'Our CEO was really fed up with not having anything to talk about at conferences when people asked, What are you doing with AI?'"

At 9fin, we use technology to support our clients in a fundamentally different way. We’ve been using AI since the company was founded in 2016 — in fact, our very first tagline was ‘AI-powered financial data extraction’.

But we’ve never believed in using AI for AI’s sake. Instead, we focus on listening to our clients and developing solutions that directly address their needs and frustrations. Our goal is simple: to help them save time, win business, and outperform their peers.

Here are three examples of how 9fin solves critical pain points to drive value for our clients.

1. AI-powered Q&A for credit analysis

We were the first to launch a ChatGPT-style Q&A tool on a credit platform, and we're regularly expanding its capabilities. Each iteration improves as we add more data sources like SEC filings, company financials, capitalisation tables, and instrument data.

Already, 9fin AI is capable of answering questions like, 'What are the key risks for this business?' or ‘What are the key events from Altice France’s earnings report?’

Importantly, 9fin has built these tools with financial professionals' needs in mind, including crucial features like audit trails.

Crucially, 9fin AI is producing better and better answers, the more types of data we integrate. Steven also hinted to what improvements might be coming to 9fin AI in the future.

“Historically, we've been very focused allowing clients to ask a question about a specific company. Soon, we may be able to tackle broader queries that analysts often need for presentations or pitch decks, like ‘What’s the current repricing wave?’ or ‘How many issuers have re-priced?’ With 9fin AI, getting these answers could take seconds. These kinds of workflows could have a major impact.”

Learn more about how customers use 9fin AI to accelerate their research in this blog.

2. Deal predictions and alerts

We also use our data to predict refinancing, restructuring and capital markets activity 12 months before it happens.

"Our deal predictions help people find transactions with specific characteristics. For example, if you want to find candidates for repricing, we can easily configure that and alert you as soon as it happens, so you don't have to check manually. Deals where the non-call period rolls off are another case where you can set up alerts to monitor in an automated way for potential business opportunities.”

3. Automated earnings call analysis

Finally, Steven reflected on the hours he’d spend dialling into earnings calls while working on the buyside, only to catch two to three minutes with real value. That’s why at 9fin, we built a tool that automatically transcribes earnings calls for you.

"We transcribe all the calls and provide an automated, real-time summary of the key points. You can have the model automatically identify the important moments, so you don't have to listen to the entire call. It saves a lot of time."

Looking ahead

For Steven, it’s impossible to predict the precise contours of this transformation over the next decade. Citing the internet as precedent, for example, he emphasised how few observers 10 years before its widespread adoption could have accurately foreseen its eventual impact.

So while crystal ball gazing remained off the cards, Steven believes firms that embrace these technologies thoughtfully will win in the long term:

As 9fin continues to develop its AI capabilities, the focus remains on solving specific problems and improving workflows to make clients' lives easier. By combining cutting-edge technology with domain expertise, 9fin is helping to shape the future of credit markets in a way that augments rather than replaces human judgment — ultimately helping clients save time, win more business, and outperform their peers.

To learn more about how 9fin's AI-powered solutions can transform your credit analysis, schedule a demo with our team.