AI Agents in Finance: Ready to Deploy

What a week it’s been in the world of AI! Among the biggest stories is the emergence of DeepSeek’s R1 model, a low-cost, open-source alternative to heavyweights like OpenAI.

While it’s shaken up the stock market and rattled some of the biggest names in tech, I believe DeepSeek’s arrival is ultimately a good thing. Affordable, accessible AI will accelerate innovation, making it easier for businesses—especially smaller ones—to adopt these tools and experiment with their applications. This kind of competition pushes everyone forward and ensures that the benefits of AI reach more people more quickly.

This edition wraps up our series on AI agents by exploring five use cases for CFOs.

Just a friendly reminder that this Thursday, January 30th, I’ll be sharing insights in a webinar AI and Finance: Beyond Chatbots with the Caprus.ai team.

This year, I am delving into the technological aspects of AI, and partnering with Caprus is an excellent opportunity to learn how AI solutions are implemented and deployed at scale.

Come join us this Thursday!

The Balanced View: AI Agents CFOs Can Implement Now

As I wrap up this newsletter series on AI agents, I can clearly see how these tools are more than just a step forward in automation. Unlike traditional automation, which focuses on managing repetitive tasks with set inputs and outputs, AI agents offer a mix of adaptability, memory, and decision-making skills.

AI agents usually work as part of a system of specialized agents, each with a specific role. They collaborate under the direction of a “manager” or “orchestrator” agent, ensuring everything runs smoothly, validating outputs, and optimizing workflows for a truly seamless experience.

For CFOs, AI agents offer great potential to streamline repetitive tasks and enhance decision-making, from compliance monitoring to financial forecasting. Though the technology is still evolving, there are numerous ways to begin embracing AI agents today!

Top Use Cases for AI Agents in Finance

Over the past year, I’ve explored the practical applications of AI in finance. While I haven’t fully deployed AI agents yet, I’ve built numerous AI automations that serve as a foundation. Here are the key areas where AI agents can deliver transformative results:

1. Reconciliation and Data Validation

AI Agent Structure:

• Data Retrieval Agent: Pulls transaction data from multiple systems.

• Comparison Agent: Matches transactions, flags discrepancies, and identifies errors.

• Mitigation Agent: Suggests corrections or provides guided steps for resolution.

Example Workflow: The AI agent autonomously handles multi-entity reconciliations—retrieving data from various systems, converting it into standardized formats, reconciling transactions, suggesting corrections, and even posting adjustment entries before preparing a consolidated report.

2. Financial Reporting Automation

AI Agent Structure:

• Data Aggregation Agent: Collects data from internal systems and external sources.

• Formatting Agent: Organizes the data into predefined templates.

• Narrative Agent: Drafts insights and key highlights.

• Visualization Agent: Produces charts and dashboards for easy communication.

Example Workflow: The AI agent aggregates internal financial data, pulls relevant market updates, analyzes company performance, drafts a narrative report with visuals, and, after reviewing it, automatically sends it to stakeholders.

3. Expense Categorization and Monitoring

AI Agent Structure:

• Categorization Agent: Classifies expenses based on predefined rules.

• Anomaly Detection Agent: Flags unusual patterns or outliers.

• Approval Agent: Routes flagged items to the appropriate stakeholders.

• Booking Agent: Posts approved transactions to the accounting software and initiates reimbursements if needed.

Example Workflow: The AI agent processes invoices autonomously, classifying them, checking compliance with internal policies, flagging discrepancies for review, and handling approvals and reimbursements.

4. Forecasting and Budget Refinement

AI Agent Structure:

• Act vs. Forecast Analysis Agent: Retrieves actuals, identifies discrepancies, and compares them to forecasts.

• Driver Analysis Agent: Highlights key factors influencing variances.

• Scenario Modeling Agent: Builds forecast scenarios based on historical and market data.

• Feedback Integration Agent: Refines forecasts with stakeholder input.

Example Workflow: The AI agent analyzes variances between actuals and forecasts, identifies the drivers, suggests adjustments for future planning, and generates scenario-based forecasts.

5. Compliance and Risk Management

AI Agent Structure:

• Regulatory Monitoring Agent: Tracks changes in regulations and flags potential compliance issues.

• Risk Scoring Agent: Evaluates transactions for exposure and prioritizes follow-ups.

• Compliance Reporting Agent: Produces audit-ready documentation with validated data.

Example Workflow: The AI agent monitors financial transactions in real time, identifies high-risk activities like duplicate invoice submissions or unusual payment patterns across subsidiaries, flags these for fraud review, and automatically prepares compliance documentation to address regulatory requirements.

What Do AI Agents Look Like in Practice?

AI agents come in four primary forms:

1. Custom AI Agents: tailored solutions built to address your company’s unique workflows.

2. Pre-built AI Agents: Platforms like ServiceNow, Salesforce, etc., provide pre-built agents for finance that can be customized and adapted to meet specific needs. 

3. SaaS Solutions: Ready-to-use third-party platforms designed to handle specific processes: Hyperbots for invoice processing and approvals, Datarails for FP&A tasks, etc.

4. Features in Existing Software: AI capabilities embedded in platforms like SAP and Workday Adaptive Planning, such as predictive forecasting modules. These are not yet agentic, but they are moving in this direction.

These forms aren’t mutually exclusive—they’re complementary. For example:

• Use SAP’s predictive forecasting module for scenario analysis.

• Pair it with a custom agent to reformat and prepare the data for input.

Understanding Levels of AI Agency

AI systems can integrate at varying levels of autonomy to match organizational readiness:

• Low Agency: Handles specific, repetitive tasks without adaptation, like transaction reconciliation or data entry automation.

• Moderate Agency: Adapts to changing inputs and provides context-aware recommendations, such as identifying spending anomalies or analyzing trends.

• High Agency: Manages complex workflows with minimal oversight, such as autonomously handling a procure-to-pay cycle from purchase orders to payment processing.

Organizations typically start with low-agency systems and gradually scale, fostering trust in AI’s capabilities and refining processes over time.

AI agents have moved beyond experimental phases and now serve as effective solutions to genuine problems. By grasping the various degrees of AI agency and creating customized agent frameworks, CFOs can strategically automate workflows, enhance efficiency, and prepare their organizations for growth.

Begin with small initiatives, fine-tune your processes, and expand confidently, assured that AI agents will develop in tandem with your business.

News of the Week: A Crazy Week in AI News

1. DeepSeek AI: A New Frontier in Open-Source Innovation

DeepSeek’s R1 AI model is shaking things up—not just in the stock market but also in how accessible AI is becoming. Sure, its rise has rattled U.S. tech giants, but let’s focus on the upside: Affordable, open-source AI is a huge win for everyone. By lowering the cost of entry, DeepSeek opens the door for more organizations to dive into AI and start experimenting. My take is that we will ultimately benefit from this kind of accessibility to accelerate learning and adoption.
Read more at Business Insider.

2. OpenAI Operator: Let’s Get Serious About Business Applications

OpenAI’s Operator is all over the internet, with stories of it booking restaurants, flights, and event tickets. Sure, it’s fun. But the real potential lies in its business applications. Imagine an AI agent that pulls reports from QuickBooks, automates expense postings, or reconciles transactions without manual intervention. That would be a game-changer for small businesses and CFOs. While we’re not there yet, I believe Operator is a step toward a future of 'Agentic AI in every computer.'
Read more at The Economic Times.

3. Stargate Project: Will More Compute Still Be the Answer?

The $500 billion Stargate initiative, led by OpenAI, Oracle, and SoftBank, aims to cement the U.S. as an AI leader by constructing large data centers in Texas. As a proud Texan, I’m excited to see our state at the forefront of AI innovation. However, there's an interesting development: DeepSeek recently showcased that breakthroughs might not require extensive computing power. Their efficient, cost-effective model questions the belief that AI dominance depends on building larger and faster infrastructure.
Read more at AP News.

Closing Thoughts

As we close this edition, we’re also wrapping up our newsletter series on AI agents. Over the past few weeks, we’ve explored their potential, practical use cases, and how CFOs can begin integrating them into their operations. I hope you’ve found this series insightful and inspiring as you think about what AI can do for your team.

Thank you for being part of this journey, and as always, feel free to reach out with your thoughts or questions.

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Until next Tuesday, keep balancing!

Anna Tiomina 
AI-Powered CFO