What is agentic AI and why does it matter for finance?
You've probably interacted with a basic AI agent before - whether it was a customer service chatbot or an AI tool for generating content. While these early agents were limited, recent advancements in generative AI models have allowed for far more sophisticated AI agents. Today, they can plan, collaborate, and manage complex workflows, continuously learning to improve their performance.
However, these agents can't function independently. They require high-quality data to work effectively, meaning they must be either embedded directly within the finance systems businesses already use or be seamlessly integrated into existing platforms.
How do AI agents work in finance?
AI agents perform tasks autonomously, based on instructions, data, and predefined objectives. Here's how they operate:
Initial input: A user assigns a task to the AI agent, which autonomously plans and determines the best way to complete it.
Data processing and analysis: AI agents access structured and unstructured financial data (e.g., transaction records, historical reports) and use machine learning and natural language processing (NLP) to interpret the data, finding patterns or anomalies that might otherwise go unnoticed.
Task execution: The agent moves to execution by automatically gathering data, formatting it, highlighting key insights or triggering actions like sending alerts for discrepancies.
Learning and improvement: Over time, AI agents refine their models, learning from new data and improving accuracy. For example, fraud detection agents improve pattern recognition as they process more transaction data.
What are some financial use cases for AI agents?
AI agents are already transforming many aspects of financial operations, providing smarter, faster, and more efficient solutions. Here are key use cases where AI is making an immediate impact: Automated reconciliations : AI agents cross-reference data from multiple sources, flagging discrepancies for human review, speeding up reconciliation processes.
: AI agents cross-reference data from multiple sources, flagging discrepancies for human review, speeding up reconciliation processes. Real-time financial monitoring : AI agents monitor transactions, flagging anomalies in real time for fraud detection and expense management.
: AI agents monitor transactions, flagging anomalies in real time for fraud detection and expense management. Tax compliance and reporting : AI automates tax calculations, and as it advances, it will dynamically adapt to changing tax laws, improving compliance.
: AI automates tax calculations, and as it advances, it will dynamically adapt to changing tax laws, improving compliance. Cost optimisation : AI agents analyse spending data to recommend cost reductions, helping finance teams optimise budgets.
: AI agents analyse spending data to recommend cost reductions, helping finance teams optimise budgets. Cash flow optimisation : AI agents predict cash flow and optimise working capital, ensuring businesses have the resources they need while maintaining profitability.
: AI agents predict cash flow and optimise working capital, ensuring businesses have the resources they need while maintaining profitability. Debt collection management: AI tools automate follow-ups on overdue accounts and prioritise collections, improving recovery rates.
The future of agentic AI in finance
The development of AI agents has been extremely fast. Looking ahead, they're about to get even as they play an increasingly central role in transforming day-to-day operations and strategic organisational decisions. Here's what we can expect in the coming months and years:
AI agents as autonomous decision-makers
In the very near future, AI agents will move beyond performing tasks to becoming autonomous decision-makers. They will be able to continuously analyse financial data, make real-time adjustments to processes, and even initiate corrective actions without human input. For example, an AI agent could automatically adjust a company's cash flow projections based on new market conditions or recalibrate budgets in response to unexpected financial shifts.
Advanced collaboration and orchestration
We will soon see AI agents working together in more sophisticated ways. For example, a network of agents across different financial departments - budgeting, reporting, forecasting, and compliance – could all collaborate to provide a unified, real-time view of a business's financial health.
Proactive financial risk management
By continuously monitoring financial transactions and external factors, AI agents will be able to predict financial risks, such as fraud, market volatility or regulatory changes, before they happen.
Smarter financial forecasting and strategy
Through advanced predictive analytics, AI agents will be able to simulate various economic scenarios and recommend the best course of action. They will also adapt and learn from past decisions, continuously refining their forecasting models to offer more accurate insights.
Ready to make AI a core part of your finance strategy?
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