AI in Finance - Generative and Agentic Models

Artificial Intelligence (AI) is transforming finance—not just by speeding up calculations or automating tasks, but by changing the very nature of financial analysis and decision-making. Among the latest advances are two powerful AI paradigms: Generative AI and Agentic AI.

These aren’t just buzzwords. Generative and agentic AI are reshaping how financial data is interpreted, how reports are created, and even how autonomous financial decisions are made. In this blog, we’ll unpack both concepts and explore what they mean for finance professionals—from accountants and analysts to CFOs and auditors.

Whether you’re building predictive models, preparing month-end reports, or assessing investment risk, understanding these technologies can help you stay ahead of the curve in a fast-evolving landscape.

What is Generative AI?

Generative AI refers to algorithms and models that can create new content—text, images, code, or even audio—based on patterns learned from existing data. Think of it as a hyper-intelligent assistant that doesn’t just analyze data but generates useful outputs from it.

In Finance, Generative AI Can:

  • Generate financial reports in natural language
  • Summarize quarterly earnings calls for internal distribution
  • Write narrative explanations for dashboards and KPIs
  • Create synthetic datasets for stress-testing models
  • Draft investment memos based on internal and external data

These capabilities are powered by models like GPT (Generative Pre-trained Transformer), which can be fine-tuned on industry-specific datasets for accuracy and compliance.

Example: A financial analyst can ask a generative AI tool:

“Write a summary of revenue performance across our top five product lines for Q2, highlighting anomalies and possible drivers.”

The AI will read your data (if integrated properly), generate a summary, and even propose questions for follow-up analysis.

What is Agentic AI?

Agentic AI takes things a step further. It describes AI systems that act as autonomous agents—able to perceive, decide, and execute tasks toward a goal, often with minimal human input.

Whereas generative AI is about content creation, agentic AI is about goal-oriented action.

In Finance, Agentic AI Can:

  • Monitor and flag unusual transactions in real time
  • Continuously reconcile ledgers with ERP systems
  • Execute trades based on predefined rules and market signals
  • Manage financial workflows, such as invoice approvals or budget reforecasting
  • Trigger alerts and suggest actions when key thresholds are crossed

These agentic systems combine machine learning, logic-based automation, and decision-making frameworks to operate semi-independently—essentially becoming digital co-workers.

Key Differences: Generative vs. Agentic AI

Feature Generative AI Agentic AI
Focus Content generation Goal-directed task execution
Output Text, code, images, summaries Actions, decisions, workflows
Interaction Model Prompt-based (e.g., chat interface) Trigger- or rule-based agents
Financial Use Cases Reporting, explanations, insights Automation, reconciliation, alerts
Autonomy Low to medium Medium to high

These approaches are often complementary. For instance, an agentic AI could detect a cash flow issue and ask a generative AI to draft a report summarizing the finding.

How Generative AI Supports Finance Professionals

1. Faster Reporting and Narratives

Instead of manually typing up commentary for management reports, generative AI can analyze financial statements and generate insights automatically.

Example:

  • “Revenue increased 15% quarter-over-quarter, primarily driven by strong sales in the North America region.”

This is especially valuable in FP&A teams, investor relations, or compliance reporting.

2. Natural Language Querying

With tools like ChatGPT or embedded AI features in platforms like SAP Analytics Cloud or Microsoft Power BI, finance professionals can ask questions like:

“What were our top five cost centers last month, and how do they compare to the forecast?”

This dramatically lowers the barrier for data exploration.

3. Document Automation

Generative AI can populate audit memos, tax filings, or investment reports with real-time data, reducing time spent on routine documentation.

How Agentic AI Elevates Financial Operations

1. Continuous Monitoring

Agentic systems can actively scan data sources 24/7, flagging exceptions like duplicate invoices, late payments, or unusual trends.

Example: An AI agent might monitor your ERP system and alert you:

“Vendor ABC’s invoices have increased by 40% this month—above your monthly tolerance range.”

2. Task Automation

Agentic AI can initiate and complete workflows such as:

  • Approving expenses under a certain threshold
  • Matching bank transactions to ledger entries
  • Re-forecasting budgets when assumptions change

It acts without needing to be told how to do each step—it understands the goal and adjusts its actions accordingly.

3. Human-in-the-Loop Governance

These systems can be configured to act autonomously for low-risk decisions but escalate to humans when needed, keeping you in control of financial integrity.

Real-World Example: Monthly Close Powered by AI

Imagine a global company automating its month-end financial close. Here’s how both AI types play a role:

  • Agentic AI runs daily ledger checks, flags mismatches, and triggers workflows for reconciliation.
  • Generative AI creates a draft of the month-end commentary, complete with charts, anomalies, and key highlights for executives.

Instead of days of manual work, finance teams focus on validating and interpreting the AI’s output—freeing time for higher-level analysis.

Risks and Considerations

AI in finance must be applied responsibly, especially when dealing with sensitive data and regulatory requirements.

  • Data privacy: Ensure AI tools comply with GDPR, SOX, or other local laws.
  • Bias and hallucination: Generative AI can sometimes produce inaccurate information. Always validate critical outputs.
  • Auditability: Agentic systems must provide logs and rationales for their actions.
  • Change management: Employees may need upskilling or role redefinition as AI takes on more operational tasks.

Getting Started: Skills for the Future

Finance professionals don’t need to become data scientists, but a working knowledge of AI concepts can go a long way. Here’s what helps:

  • Understanding prompts: Learn how to interact with generative AI for best results.
  • Process mapping: Identify workflows ripe for agentic automation.
  • Data literacy: Know your source systems, data models, and governance needs.
  • Ethical awareness: Be mindful of responsible AI use in finance.

Training resources like Coursera, openSAP, or company-sponsored bootcamps are great starting points.

Conclusion

Generative and agentic AI are not science fiction—they’re already at work in modern finance departments. These technologies are helping teams move from manual, repetitive tasks to strategic, insight-driven decision-making.

Generative AI empowers professionals with faster reporting, deeper insights, and smarter communication. Agentic AI acts as a digital coworker—executing workflows, monitoring data, and helping ensure financial control.

Together, they create a new paradigm where finance isn’t just about numbers, but about intelligence, agility, and impact. Forward-looking teams that embrace these tools now will be well-positioned to lead the finance function of the future.