Global Fintech Interview with Steven Song, CEO and Founder, Diald AI

Steven Song, CEO and Founder at Diald AI shares more on how fintech innovations are influencing real estate estimations and workflows in this Global Fintech Series interview:

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Hi Steven, tell us about yourself and what inspired Diald AI?

I’ve spent my career around real estate, investing, and building technology, so I’ve seen how decisions actually get made when real money is on the line.

What always struck me was how much of real estate risk lives outside the spreadsheet. It’s in policy changes, neighborhood sentiment, local narratives, and similar variables investors commonly talk about but can’t really quantify. Investors know those things matter, but they usually get handled informally, almost as gut checks alongside the numbers and quantitative side of the equation.

I kept coming back to the same question. If everyone agrees these factors influence outcomes, why isn’t there a consistent way to analyze them?

Diald grew out of that. The platform scans millions of data points across news, regulatory sources, local sentiment, and market coverage, and turns those signals into a structured report investors can easily use when evaluating a site or property. We built it to make that information measurable and usable in the underwriting process, so investors aren’t relying on instinct alone when they’re trying to understand what could actually impact a property over time. 

How are AI powered fintech platforms like Diald AI changing the scope of investment analysis?

AI is changing what investors are able to pay attention to.

For a long time, investing in the real estate space has focused on financial history, comps, and market data. That’s still important, but it leaves out a lot of the context that ultimately decides how an asset performs. Things like regulatory tone, public sentiment, and local dynamics have always influenced outcomes, they just haven’t been part of a structured analysis.

What AI makes possible is bringing those indicators into the evaluation process in a practical way.

That shifts how decisions get framed and expands the lens investors use to evaluate risk and opportunity. Two properties can look nearly identical on paper, but feel very different once you understand the environment around them. AI helps surface that difference in a way that’s consistent and scalable.

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What should end users be considerate about with the increased use of AI in real estate markets and evaluation processes?

A lot of AI tools today are designed to feel helpful. They’re conversational, adaptive, and good at agreeing with you. That’s great for general use, but it can be a massive problem when you’re making investment decisions.

In real estate especially, it’s easy to ask a question in a way that leads you toward the answer you already want. If you keep rephrasing the same prompt, many systems will keep giving you versions of that answer. Over time, that can reinforce assumptions instead of challenging them.

Investors don’t really need AI that thinks like they do. They need AI that pushes back.

In that way, the value of AI comes from consistency and clarity, not personality. The best systems are the ones that force you to confront signals you might otherwise overlook, and that stay grounded in the data rather than adapting to what you hope to see.

What fintech trends will shape the fintech ecosystem in the next few years?

We’re starting to see fintech show up earlier in the decision-making process for investors.

Up until now, most tools have been built around execution, reporting, and back-office efficiency. What’s changing is that AI is beginning to influence how investors size up risk and opportunity before capital is deployed, especially in private markets and real assets where context plays a big role.

At the same time, explainability is becoming a requirement. Investors want to understand how a system reaches its conclusions, not just accept the output at face value. The tools that gain mass adoption will be the ones people can question, test, and defend when they’re making high-stakes decisions.

Can you talk about some of the most impressive fintech tools from around the world that have piqued your interest and why?

I tend to notice fintech platforms that fix problems people have just gotten used to working around.

Carta is a good example. Ownership information in private markets used to live across emails, legal documents, and a lot of institutional memory. Carta pulled all of that into one system that people can actually rely on day to day.

Bilt Rewards stood out to me for a different reason. Rent is one of the largest recurring expenses tied to real assets, and they turned that everyday payment into something that connects credit, loyalty, and data in a way that just makes sense once you see it.

What connects both of them is how practical they are. At a high-level, they make information easier to use, which is a philosophy we share at Diald.

Five fintech takeaways you’d leave our readers with before we wrap up.

1. AI should challenge investors, not comfort them. The systems that add value surface considerations you might otherwise overlook and help counter confirmation bias. If a tool always seems to agree with you, it’s probably not asking enough of you as a decision-maker.

1. Decision support will outpace automation. Speed is nice, but the bigger win is better judgment, especially in the gray areas where the data looks fine but the true risk sits in the deeper context. The best tools will help investors pressure-test assumptions before money gets put to work.

1. Qualitative data is becoming institutional-grade. Things like zoning and land-use language, and public sentiment used to live in the background as “soft” considerations. Now they can be measured and brought into analysis in a way that meaningfully informs how risk is evaluated.

1. Explainability is the price of adoption. If someone can’t understand how an AI-powered system reaches its conclusions, they won’t feel comfortable relying on it for meaningful decisions, particularly in private markets.

1. Precision is going to matter more than personality. As AI becomes more common, the tools built for rigor and consistency, rather than conversation, will be the ones that influence where capital gets deployed.

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