How Payhawk Turned Seven-Year AI Vision Into Reality
Payhawk has moved beyond simple experimentation to deploy AI agents that reduce month-end closing times by up to four days. The Bulgarian expense management platform shares how its long-term AI goals are finally being realized.
When Chief Financial Officer Konstantin Dzhengozov and his co-founders started Payhawk seven years ago, they had a bold vision. They wanted a chatbot capable of answering questions like, "Where is my invoice?" or "What amount do I need to pay this supplier?" and providing expense and cash flow forecasts for the next month.
At the time, these ideas were ahead of the technology available. "Seven years ago, it was just an idea, and the technology was not there yet," says Dzhengozov.
From Experimentation to Implementation
Today, the situation has changed significantly. Payhawk has moved past the early experimental phase common to many fintech companies exploring artificial intelligence. "Right now we are past the phase of just experimenting and trying different things out, and the technology is there so we can build on this," Dzhengozov explains.
The company is actively developing and deploying AI capabilities that fulfill its original ambitions. However, working in financial services means contending with evolving regulations.
"This is still the very early stage. It is going to be maturing more in the upcoming years as AI in general is still very early stage," Dzhengozov notes. Because Payhawk handles payments, it must maintain specific audit trails, permissions, and rules. These requirements shape the AI architecture, ensuring it fits within existing controls and frameworks.
Measurable Impact on Financial Operations
Early results show tangible benefits for Payhawk’s clients. The company has seen significant efficiency improvements, especially in month-end closing processes.
"One of the clear indicators early on that we are seeing is that companies can close their month much faster, usually two, three or four days quicker than before," says Dzhengozov. This extra time allows finance teams to focus more on analyzing performance, stress testing financials, and preparing different scenarios.
These gains demonstrate how AI can streamline financial operations, providing practical value beyond theoretical potential.
- Faster month-end closing by up to four days
- Improved ability to analyze financial performance
- Better preparation for financial scenarios and stress tests
For professionals interested in AI applications within finance and development, exploring courses on AI implementation can be valuable. Resources like AI tools for finance offer practical insights into these technologies.
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