Trust, Not Speed,
Is the Real Currency of AI in Finance
Vladislava Gaeva, Lead, Financial Services, Sirion
Vladislava (Vlada) Gaeva prefers routine, outside work. She goes to the gym, takes the same route for walks, and keeps a close circle of friends and family.
“It’s how I prefer things to be. I like to keep things simple and structured,” says Vlada.
Vlada grew up in Russia, in a household where law was part of her daily life. Her father was a lawyer. Early on, she decided to follow the same path. It felt natural. Law offered a way of thinking that was precise and contained.
"There's something in me that enjoys a good debate; that wants to reason things out to the end. I've always been drawn to problem-solving. In law, those instincts finally have a use—the vocation doesn't just give you answers; it teaches you how to think."
The Shift From Legal to Finance
At seventeen, Vlada moved to the U.K. to study. She finished her bachelor's in law at the University of Exeter and then moved to London to complete her master's degree.
In her early days, Vlada worked across different legal roles—immigration, litigation, and in-house. She learned the mechanics of each. But she didn’t settle into anything.
Traditional legal work felt contained and slow. After trying multiple roles, she searched for "something that would create the spark early in my career."
Finance gave her that. Before joining Sirion, Vlada worked at Goldman Sachs. "The move to finance was driven by curiosity. I never pictured myself at an investment bank," she says. "I enjoy taking on challenges. It was just a 'why not try it' moment."
Understanding the system
The Goldman Sachs role put her inside operations, working on derivatives documentation. Her job was to assess complex contracts. She extracted, validated, and tracked structured data.
"The work itself was straightforward. The scale was not," she recalls. "At one point, the team was processing thousands of agreements manually."
It was in that environment of repetitive, high-stakes, and unforgiving at volume that she came across a demo on automating contracting work. Something shifted.
During the demo, she was impressed by how AI can change the way she and her teams work. She was quick to add, “It was a new way to approach it.” She continues, “Working with large volumes of contracts manually. It was repetitive and time-consuming, and it was clear that a lot of that work didn’t need to be done that way.”
It didn’t change what she was doing that day. But it changed how she thought about the work.
Legal, Contracts, and AI in Financial Services
In financial services, contracts have to be traceable, while decisions have to be explained. That's just the nature of work.
"The question used to be whether to use AI at all," she says. "Now it's more specific, where can you actually rely on it, and how do you verify what it's telling you?"
Vlada understands why hesitation exists. The work is layered. Two people can read the same contract and come away with different interpretations. Any tool that sits inside that process has to do more than give you an answer. It has to show you how it got there.
"That's the part that matters," she says. "Not just the output, and the reasoning behind it."
Influencing the World of Financial Contracting
Financial services are under constant scrutiny. Regulations evolve. New requirements emerge. The same contract must hold up across everything.
“Trust, in financial services, is built through familiarity. Validate, understand why a certain section is flagged and step in if it doesn’t look right,” explains Vlada.
It starts with complex agreements—sometimes hundreds of pages. There is back-and-forth via email. Versions stack up. Clauses are negotiated in isolation. Context sits in someone’s inbox.
Then the document moves across teams.
“Legal reads it one way. Risk reads it another way. Compliance looks for something else. Each team is accountable for a different outcome. They work from the same contract but do not always share the same understanding,” she adds.
The contract, in that sense, must do more than sit in a repository. It has to work across audiences. Vlada explains. “A good CLM should answer basic questions without people having to dig through documents. What are we committed to? What are we paying for? What’s coming up next? If teams still rely on separate trackers or manual checks, the system isn’t doing enough.”
The system needs people who not only understand the process but are prepared to make it efficient.
Large financial institutions faced with new technology often confront what Vlada calls the "build or buy" dilemma. "When you're tasked with a team of engineers in a bank, that's very different from a team of more free-thinking and enthusiastic engineers at a specialized firm actually focused on the product," she says. "Their engineers are also, in a sense, regulated."
Bridging the Trust Gap in Financial Services
Vlada’s profile as lead in financial services at Sirion is about translating how the product fits into a world she knows well.
She works closely with a team from the same environment. These are people who have worked in financial institutions, including banks and insurance firms.
“We’re coming from that world,” she says. “We understand how it works.”
That perspective shapes their engagement. They are not outsiders introducing change. They are operators who have worked within the same constraints.
Vlada describes a meeting with a large investment bank. They had evaluated multiple vendors and were still unsure what would actually work.
She and her team walked through the platform in practical terms. Not features, but how the work would change. They showed how contracts could be read as a connected system. Data could be pulled from agreements and shown alongside negotiation. Related contracts could be understood together, not in isolation.
“That's when the conversation changed,” she says.
What shifted was not the product but the approach. For Vlada, “that” change is incremental. “It reflects how change happens in financial services—slowly, carefully, and only once trust is established.”
“The atmosphere in the room changed.”
That was a win for Vlada. “Even today, companies are restricted to CLMs that just store documents, but don’t help much beyond that. Teams often end up using spreadsheets or separate trackers to manage what actually matters.”
So when demos and conversations are to change the conception of AI in Finance, Vlada celebrates it quietly with her team. While she prefers things simple and structured, and maybe that's exactly why she knows which changes are worth making room for.
Rapid Fire
Facts about AI in Finance
with Vlada
Biggest Misconception About AI in Financial Services
That it just makes things faster out of the box. Most of the time, the issue isn’t speed. It’s messy inputs and inconsistent ways of working. If that’s not addressed, AI just processes the same problems more quickly.
The Real Barrier to Adoption: Regulation or Mindset?
Mostly mindset. Regulation matters, but people are used to working a certain way and are cautious about changing it. That’s usually the bigger blocker.
Build vs Buy?
Most companies start with the idea of building internally. It is mainly for control. However, in practice, it’s difficult to maintain and takes a lot of time and resources. So, firms usually end up with a mix of internal and external tools, which isn’t always ideal. That’s where platforms like Sirion stand out. It gives you a more complete enterprise solution and still works well alongside existing internal systems, rather than trying to replace everything outright.
The Biggest Challenge in Financial Services: Getting contract data to be consistent and usable across large volumes. It’s not just extracting information. It’s making sure different teams can rely on it in the same way.
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