Discover top fintech news and events!
Subscribe to FinTech Weekly's newsletter
Read by executives at JP Morgan, Coinbase, Blackrock, Klarna and more
Agentic artificial intelligence (AI) is stepping into finance with real autonomy. Smarter models and open application programming interfaces (APIs) are accelerating this shift, kicking off a race to own this new level of automation. So, who is funding the next wave of autonomous financial assistants?
What Is Agentic AI in Financial Services?
Agentic AI in finance is software that can chase a goal with limited human help. It plans steps, performs tool calling through APIs and takes safe actions by pausing for approval when rules require it. These agents are a more advanced version of a basic chatbot. With greater skills, agentic systems can run a plan-act-check loop and follow guardrails.
This new autonomy layer can work like humans by handling tasks like:
- Collecting documents and running Know Your Customer checks
- Watching transactions and halting risky payments while alerting compliance
- Sweeping extra cash and scheduling payments
- Taking on finance team work, like forecasting and variance analysis
Because of agentic AI’s capabilities, it's already showing up in pilots and early rollouts across retail banking and fraud services. NVIDIA’s industry report has even found that 90% of surveyed respondents reported a positive impact on their company’s revenue due to a better customer experience.
What Is Facilitating This Change?
Agentic AI is taking off in finance because the stack finally works end-to-end. A few key forces are what make autonomous assistants practical in production, and it’s why funding is chasing the teams productizing these capabilities:
- Smarter base models: Better reasoning and tool use make agents reliable enough for real tasks.
- Cheaper, faster compute: Training and inference costs keep dropping, so pilots can scale without wrecking unit economics.
- APIs to real money movement: Banking, payments, market data and core systems now expose secure APIs, so agents can act rather than only advise.
- Agent orchestration tools: Planners, memory and evaluators help agents break work into steps and ask for approval when needed.
- Guardrails and auditability: Policy engines and logging make actions traceable and compliant.
- Enterprise data readiness: Cleaned data and retrieval pipelines allow for the safe use of private context.
The Funding Landscape
With the tech stack mature enough for production use, capital is moving in to claim the first real wins. Venture funding for AI startups has increased by 75.6% in the first half of 2025, hitting $162.8 billion, which makes this the best performance this market has seen since 2021.
This record-breaking figure shows how quickly investor priorities have moved toward autonomy and intelligent systems. Fintech is a major part of that push. After a slow 2023, the sector saw funding rebound above $10 billion in the first and second quarters of 2025 — the first time it’s held that level in nearly three years.
Seasoned allocators are also applying dot-com era discipline. During the early 2000s boom, startups famously poured about $44 million into Super Bowl ads in a year, grabbing attention but burning through capital before proving long-term value. That kind of energy left a mark on investor thinking, leading today’s backers to favor agentic AI ventures that can provide efficiency gains and regulatory readiness before scaling aggressively.
As a result, money isn’t spreading evenly. It’s concentrating on where agentic systems can plug into existing rails with limited regulatory friction. Early funding is going to teams that pair real distribution in financial services with credible autonomy roadmaps, instead of clever demos.
Who’s Writing the Checks?
The sources of funding for this work are varied. Generalist, multistage venture capitalists are making the bigger bets, but only when a team can demonstrate working pilots inside real bank workflows.
One example is Samaya AI. This AI financial service platform announced $43 million in financing, led by New Enterprise Associates and fintech leaders like former Google CEO Eric Schmidt and AI Turing Award winner Yann LeCun. The company will use this funding to build expert agents for financial services, which signals that top-tier capital will back autonomy when tied to regulated use cases.
Strategic investors from finance follow once there’s a path to scale with controls. In June 2025, Goldman Sachs Alternatives led Conquest Planning’s $80 million Series B, with major banks participating and backing planning tools that boost advisor productivity. Funding typically targets areas that reduce risk and unlock new revenue under audit.
Finally, programs that open doors to pilots matter as much as cash. Mastercard Start Path’s new security cohort focuses on fraud and identity startups, while Visa accelerator connects fintechs to payments rails across regions. These routes turn a demo into a bank-ready deployment, and that proof is what helps with reaching the next round.
What Ethics and Rules Mean for the Financial Advisory Sector
Autonomous AI must follow the same laws and standards governing human advisors. These rules involve suitability, fair communication, data protection and risk management. In the U.S., that means agents used for recommendations or transactions should be supervised and tested for accuracy.
Ethics are also important. These tools must be transparent about when clients interact with AI and avoid hidden bias in their recommendations. They must also keep humans in control of final calls, especially on money movement or investment changes. Without these safeguards, errors can lead to financial loss and reputation damage.
Under financial advice and management, the advisor's role is changing. They can now act as editors and decision-makers, reviewing AI-generated recommendations, adding human context and ensuring they meet the letter of the law. Done right, agentic AI could help the industry serve more clients faster while keeping trust and accountability intact.
Keeping Autonomy Accountable
Agentic AI is ready for finance, but trust comes first. There must be proof that it’s in control and accurate while providing a good return on investment. Teams demonstrating strong data access and compliance will be the first to receive capital.
