.webp)
AI agents in finance and accounting are transforming operations by executing real tasks, not just answering questions. With built-in logic, accuracy safeguards, and action toolkits, these agents automate processes like transaction matching, reporting, and data entry—freeing finance teams to shift from manual work to strategic review and oversight.
AI agents in finance and accounting are no longer a futuristic concept or just another buzzword. They’re actively changing the way teams operate, from automating journal entries to generating real-time financial insights.
These systems don’t just provide answers—they perform tasks and offload manual work.
In our recent Bottom Lines webinar, Nominal’s CEO, Guy Leibovitz, explained how AI agents are built, what makes them different from assistants like ChatGPT, and how they’re already delivering tangible value to accounting teams.
He walked through real examples that highlight how agents can improve accuracy, reduce errors, and free up capacity for more strategic work.
One of the most important takeaways: finance doesn’t need an autopilot, it needs a co-pilot. AI agents support human decision-making by handling the legwork while professionals stay in control.
With rising complexity and a shrinking talent pool, this shift is becoming a critical advantage for forward-thinking finance teams.
If you're curious about how AI agents actually work—and where they fit in your workflow—keep reading. This article breaks down what you need to know to stay ahead of the curve.
How is AI used in finance and accounting?
Artificial intelligence has become a strategic tool for finance and accounting teams, helping streamline operations, improve accuracy, and reduce time spent on repetitive tasks.
From automating invoice processing to forecasting cash flow, AI is being applied to both tactical and analytical functions across the finance function.
Traditionally, most AI applications in accounting have centered around pattern recognition, predictive analytics, and anomaly detection.
These systems analyze large volumes of financial data to identify trends, flag inconsistencies, and support decision-making.
They’ve been especially helpful in areas like audit preparation, expense categorization, and real-time reporting.
While these use cases have delivered measurable efficiency gains, the next evolution of AI in finance is moving beyond insights and into execution. This is where AI agents come in: offering a new model for task automation and system interaction.
What Are AI Agents in Finance & Accounting and How Are They Different?
Unlike general AI assistants that primarily retrieve information, AI agents in finance and accounting are built to take action.
They don't just respond to prompts; they perform tasks, follow logic, and interact directly with systems to execute work that would otherwise require human input. They rely on a few core components to function effectively:
- Triggers: A defined event—such as a new journal entry, a file upload, or a scheduled action—that activates the agent
- Stepwise instructions: A clear sequence of tasks the agent follows, similar to how a human would be trained on a process
- Critics: A built-in self-correction layer that flags inconsistencies and reruns tasks to improve accuracy, often increasing output quality from 40% to over 90%
- Action toolkits: The agent’s capabilities, such as connecting to a general ledger, generating reports, or reconciling data
As Guy explained:
"If ChatGPT was a know-all Oracle, agents are the next evolution. They don't just answer questions—they take initiative and offload the manual work."
In practice, this means agents can handle recurring, structured tasks like transaction matching or accrual entries, all while maintaining transparency and auditability, something passive assistants aren’t equipped to do.
Copilot vs. Autopilot: The Right Approach for Accounting
A key takeaway: AI should act as a copilot, not an autopilot. While autonomous systems like self-driving cars take full control, accounting demands a more collaborative approach.
The most effective implementations shift finance teams from doing to reviewing, letting AI handle the legwork while humans ensure accuracy and compliance.
AI Agent Use Cases in Finance and Accounting
One of the most compelling parts of the webinar was the live demonstration of how AI agents can be applied in real finance workflows: not in the future, but right now. These weren’t conceptual use cases or pilot programs.
They were hands-on examples of how Nominal’s agentic AI is already transforming routine processes in finance and accounting.
1. Transaction Matching
AI agents can automatically detect, match, and reconcile transactions between entities.
Instead of manually searching spreadsheets for intercompany entries, agents surface relevant records, suggest eliminations, and prepare them for review, accelerating the close process and reducing reconciliation errors.
2. Unstructured Data Processing
When key financial inputs like payroll or expense data arrive in Excel or PDF formats, AI agents can ingest and transform this unstructured data into standardized journal entries.
This eliminates the need for manual reformatting and ensures consistency in accruals, even when source files vary by department or system.
3. Analysis and Reporting
Agents also support real-time reporting by pulling data from internal systems and generating insights on demand. Finance professionals can request a P&L comparison, variance analysis, or prior-quarter trends using natural language.
The agent selects the right data sources, applies context, and delivers structured results, without requiring technical queries or IT support.
4. Human-Agent Collaboration
Rather than replacing human oversight, AI agents are designed to work alongside finance teams. Every task performed—whether it’s a journal entry or a report—is logged with an audit trail and routed for human approval.
This ensures transparency and compliance while dramatically reducing time spent on manual inputs.
How AI Agents Are Shaping the Future of Finance
Digital labor is gaining traction just as the accounting profession faces real challenges. With 75% of CPAs set to retire in the next 15 years and exam participation at a 17-year low, finance teams are under pressure, especially as business models become more complex.
AI agents offer a timely response. By offloading repetitive tasks and supporting smarter workflows, they’re helping teams move from manual execution to strategic oversight. Here's how that shift is already starting to unfold.
Continuous Close
Instead of waiting for the end of the month to reconcile books, finance teams are beginning to move toward a continuous close.
AI agents enable this shift by processing transactions in near real time, identifying issues as they occur, and keeping records updated daily.
AI-Assisted Auditing
Audit preparation is becoming faster and more proactive. AI agents can log every step they take, creating a built-in audit trail that simplifies documentation and reduces risk.
This automation streamlines compliance processes and allows finance leaders to respond to audit requests with greater speed and accuracy.
Systems of Intelligence
Traditional financial systems have focused on storing and organizing data.
The next generation will be systems of intelligence—tools that not only record what happened but also interpret trends, surface risks, and suggest next steps. AI agents play a central role in this transition by turning static data into actionable insight.
Q&A Highlights From the Webinar
Q: Which processes should finance leaders tackle first with AI?
A: Look at two areas:
- Where you need to analyze data and create narratives (like variance analysis)
- Complex Excel workbooks with many tabs. Also, consider using AI to improve team efficiency before making new hires.
Q: What are the must-haves when evaluating AI tools?
A: First, security is important. Look for SOC 2 and SOC 1 certifications and ask about data privacy. Second, understand how they mitigate hallucinations. Start with baby steps and proceed cautiously while embracing the technology.
Q: What AI tools does Guy personally recommend?
A: Beyond ChatGPT, consider Anthropic's Claude for app creation.
The Bottom Line
“AI won’t take your job, but someone using AI will.”
That phrase captures a shift already underway in finance and accounting. AI agents aren’t here to replace professionals—they’re here to eliminate the manual, repetitive tasks that hold them back. The real value lies in giving teams more time and headspace to focus on analysis, strategy, and decision-making.
As the landscape continues to evolve, staying informed and connected becomes essential. That’s why we’re building a space for finance professionals to explore real-world AI applications, share experiences, and learn from one another.
Want to go deeper? Join our next webinar: How AI Agents Replace Spreadsheets in Modern Accounting
.webp)
