Over the past decade, finance has steadily automated repetitive processes such as forecasting cash flows, updating invoice status to ensure timely collections, accounting for accruals and expenses, and transaction management.
Yet, many of these critical finance functions still require human intervention, as most automation tools still rely on rigid workflows and static rules. Traditional automation tools present multiple challenges: rules-based bots struggle with exceptions, SaaS systems often operate in silos and real-time data integration is absent. This leads to finance teams spending time and effort in detecting and flagging anomalies and taking actions.
Moreover, the real struggle starts when businesses decide to scale with these inefficient solutions. Integrating these tools across fragmented systems creates more responsibilities for teams and often results in data inaccuracies as these tools are unable to scale efficiently. . What looks like automation on the surface level often just shifts manual work to new places.
The only way to solve this is to implement agentic AI. AI agents in finance operate independently, process ambiguous inputs, and resolve issues without escalation, allowing businesses to take a step beyond automation. This blog covers everything you need to know about agentic AI in finance – what it is, features, benefits, how it works, business applications, differences with traditional AI, and more.
AI agents in finance are intelligent digital systems that take on core finance tasks, make decisions based on context, and evolve as data changes. Unlike bots or static rules, they operate with clear goals and minimal oversight, cutting manual work and bringing full visibility across ERPs and tools.
AI agents behave more like intelligent teammates than automated tools. They differ largely from bots that only automate tasks up to a limit. They do not just run scripts but evaluate situations and make smart decisions. While executing tasks with precision, these agents continuously learn and improve without requiring constant rule reconfigurations. Moreover, these “agents” are goal-seeking by design, focusing more on the outcome they need to achieve and not requiring human fixes for every unexpected scenario.
85% of CFOs expect productivity gains from AI.
Agentic AI is what gets them to ROI.
Download Agentic AI GuideThere are three types of AI agents in finance. These agents help finance teams perform tasks more efficiently, automate repetitive work, and make decisions based on real-time data.
Analytical agents, as the name suggests, evaluate data to generate deep insights into cash flow, financial performance, and risk management.
These agents mainly focus on improving communication across systems and eliminating data silos. They handle mission-critical tasks like collection management, payment follow-ups, and responding to vendor questions.
Even with multiple automation tools, most finance teams still work through disconnected steps. Over 90% of businesses still deal with manual handoffs, approvals, and fixes because most software wasn’t designed to act—it was built to follow. Agentic AI flips that model. It doesn’t just automate a task. It transforms finance workflows in the following ways:
Agentic AI is a system capable of making decisions autonomously, leveraging intelligence and learning from past patterns. If a recurring vendor payment is low-risk, it clears it on its own. This autonomy, which comes without any nudges, cuts lags in invoice processing, cash flow visibility, projections, and remittance matching.
AI agents not only apply rules but also build judgment. For instance, if a buyer consistently pays late, the system starts forecasting based on their behaviour, not the usual payment terms. It learns, adjusts, and records such instances for future reference.
These tools don’t wait for red flags and detect issues by reading surface patterns early on. Maybe a supplier’s refund behavior shifts, or a new bank detail pops up unexpectedly. Agents proactively identify such issues and flag them while suggesting necessary actions.
Most legacy AI tools only point to problems. AI agents, on the other hand, fix them. For example, if a dispute is logged, agents will address it, record it, and close it out—and the logic gets smarter next time.
Agentic AI logs every action with precision. It applies internal policies, follows protocols, and maintains consistent records automatically. This further helps reduce the preparation time required for compliance reviews and eliminates gaps in financial reporting and documentation.
From pulling aging reports to triggering entries, AI agents complete complex task sequences without waiting for any human intervention to accelerate the process. They are more like the digital controllers: consistent, accountable, and always on schedule.
AI agents in finance go beyond basic automation. These systems don’t just move data—they interpret it, act on it, and learn from the outcomes. They’re built for decision-level delegation, not just rule-based task execution.
These agents don’t wait for direction. If a payment fails because of a missing purchase order or a mismatch, they’ll go look for it. They can cross-reference systems, flag the issue, suggest a fix, or, in many cases, just fix it outright. This not only helps clear up issues and flaws but also eliminates manual review queues.
Accurate forecasting requires a rigorous study of changing customer behaviors, preferences, and industry trends, and the adjustment of projections in real time. Agentic AI not only tracks behavioral patterns but also incorporates warning signals. As a result, businesses get accurate forecasts along with layered multiple data inputs.
You can train rules into a bot. But when things get ambiguous, like a vendor requesting early payment against past behavior, bots freeze. However, agents don’t. They weigh the context, past transactions, and risk level, then decide whether to approve, escalate, or hold.
Unlike AI assistants or RPAs that follow a one-size-fits-all approach, AI agents adapt to the changing needs of the solution and best fit the business outcomes. They will notify the businesses and update records and also own steps without tapping out. They will work across ERPs, CRMs, and ticketing tools like they were built for.
AI agents sharpen their decision-making logic with every transaction, enhancing treasury system abilities for smarter strategies. Additionally, unlike RPA, which calls for static instructions, these agents fine-tune their actions through feedback loops and operational context.
Agentic AI analyzes workflows rigorously and continuously to identify inefficiencies and suggest improvements. Over time, this helps teams to get measurable gains in cycle time, accurate reporting, and improved cost per transaction.
Businesses must realize that these AI agents are more than mere advanced chatbots. Agents don’t require explicit instructions and autonomously navigate tasks, while making robust decisions and learning from outcomes. The level of sophistication and adaptability surpasses conventional AI tools any day.
AI agents in finance are no longer experimental—they’re already transforming how finance and accounting teams manage their workloads. What’s critical is that they don’t replace finance teams but augment them. They’re much more valuable when integrated into existing workflows, where they quietly handle the repeatable stuff and surface insights in real time. That means teams spend less time chasing down data and more time acting on it. Here are a few applications of AI agents in finance.
AI agents collect accounting data, eliminate manual data entry, scan your ledgers, and flag mismatches early. This means teams spend less time reconciling accounts and more time analyzing the bigger picture. They also spot unusual transactions—things that might get buried in the noise but could raise a red flag down the line. AI agents in accounting enable businesses to accelerate days to close by 30%.
AI agents in treasury are being used to improve the accuracy of cash forecasting. They pick up subtle patterns that humans might overlook. When integrated with existing ERPs, banking systems, and other platforms, these agents provide a real-time snapshot of a business’s liquidity position. That’s a huge shift from waiting on static reports or stale dashboards. It also means businesses can make informed decisions—whether it’s investing surplus cash, drawing down credit, or holding back.
Instead of having collectors spend hours following up on every unpaid invoice, AI agents can handle the routine checks by sending friendly reminders that keep payments moving. It frees businesses from dealing with more strategic accounts and tough cases, while clocking a measurable impact on DSO.
On the back end, AI agents speed up cash applications, too. No more waiting for someone to match payments against open invoices line by line. The agent uses pattern recognition to do it quickly and with fewer errors, so cash visibility improves almost instantly.
Incoming invoices used to mean piles of manual work—data entry, matching to POs, and routing for approval. Now, agents can pull out the right fields, do the validation, and push it through the proper channels automatically, exhibiting a drastic reduction in errors. These agents are also continuously improving themselves for dynamic risk management by detecting warning signs that finance teams might miss, like duplicate invoices, mismatched vendors, or odd payment patterns.
AI agents are quietly reshaping how finance teams operate. They’re showing up in day-to-day workflows and enhancing efficiency. They have completely transformed how finance teams work.. Instead of being buried in repetitive tasks like matching payments or keying in invoice data, teams now get to focus on bigger-picture work that drives more strategic value. Here are a few core benefits of leveraging AI agents in finance.
When AI agents handle the tedious work behind finance processes such as cash application or invoice routing, the finance team isn’t bogged down by the process anymore. Workflow efficiency improves, and teams have more time to zero in on exceptions or strategic analysis.
The systems these AI agents run on are trained to flag inconsistencies that humans might miss, especially when things get hectic at month-end. For some teams, this has meant reducing reconciliation errors by more than 90%, which is huge given how costly even small mistakes can be.
With the help of agentic AI, teams are getting early warnings instead of reacting to issues. This foresight is of great significance, especially in cases where a business is trying to protect margins or avoid compliance fallout.
When businesses have fewer hands tied up with manual work, they speed up operations and reduce overhead. That capacity can be redirected toward higher-value initiatives without needing to add headcount. In the long run, this operating model shift lets finance do more with less and do it smarter, going beyond just tech upgrades.
Traditional AI and Agentic AI serve distinct purposes within finance. While traditional AI focuses on task-specific automation, agentic AI goes further, acting as a fully autonomous system that handles entire workflows. Understanding the difference between these technologies is key to realizing the potential for transformation in financial processes.
Most AI tools in finance right now are doing pretty narrow jobs. They merely predict cash flow, maybe tag a few transactions. They’re decent, but they don’t handle surprises well. If thrown off the script, they cannot troubleshoot and basically wait for someone to step in. That’s where agentic AI is different. It doesn’t just run a model and spit out numbers. It watches, it learns, and it adjusts. If it sees a potential risk? It acts immediately. And the more it runs, the smarter it gets. It’s not static, evolving with the data.
RPAs are like reliable assistants who follow instructions to the letter. They build the workflow, and it repeats it. Great for repetitive stuff, sure. But if the situation changes or escalates, like unstructured inputs or anomalies, they cannot act on their own. AI agents don’t need every rule spelled out. They recognize patterns, fill in the gaps, and make calls based on real context.
AI assistants, despite strong use cases, are still pretty reactive. They’ll answer a question, organize a task or two, but they need prompting. Agents, on the other hand, can manage entire workflows, start to finish. They don’t wait for commands. They look at the full picture, anticipate what’s needed, and just get on with it.
Automation alone isn’t enough anymore. What CFOs actually need is adaptability. Sure, traditional tools are good at handling repetitive tasks, but they cannot handle curveballs unless a business dictates how to do it.
That’s where agentic AI comes in. These systems watch what’s happening, learn from it, and adjust in real time. So instead of catching issues in crucial financial tasks like forecasting, reconciliation, and liquidity, they help prevent them. That shift means teams spend less time firefighting and more time steering the business forward.
While it’s about time for CFOs to take their operations to the next level using AI agents, they cannot dive headfirst without getting a comprehensive view of their risk and governance. The reality is, most companies aren’t fully ready for it. In fact, less than 15% have strong responsible AI practices in place, which means governance gaps are still a major issue. Before jumping in, here are a few questions CFOs must ask.
CFOs need to gauge whether the current infrastructure supports dynamic data ingestion and real-time updates. Agents should also be able to deliver smooth hand-offs between humans and systems. If not, there’s groundwork to do.
It also helps to be crystal clear on the “why.” Are you aiming for faster closes, fewer manual write-offs, or tighter cash conversion cycles? Deploying agents should directly relate to those outcomes, not just to prove technical sophistication.
Defining governance and policies needs to come early in planning, not after the fact. CFOs would want policies for their companies that clearly define what the agents can access, how they escalate issues, and where human review still needs to happen.
A good place to start is where decision rules are fairly stable and repetitive work is bogging teams down. Take cash reconciliation or financial consolidation—both are high-friction, low-judgment areas that tend to show results quickly.
Implementing advanced tech like Agentic AI goes beyond mere plugging in a tool. CFOs must ensure that the vendors they choose give the business actual autonomy rather than just a fancier dashboard. They should trace decisions back and not give logic in a black box.
Agentic AI is not a one-and-done setup. It needs quarterly check-ins with KPIs that matter to finance, like fewer errors, faster resolutions, and better compliance. CFOs who evaluate and learn continuously can make the adoption go smoother and avert the risks on time.
Most AI solutions for finance don’t operate inside the workflows—they work around them. They analyze and suggest measures. But when it’s time to act, they wait. That’s the gap. Finance doesn’t require another dashboard or assistant. It requires systems that do the work autonomously using data-driven insights.
HighRadius solves this by embedding intelligent, decision-capable agents directly inside the platforms where finance teams operate daily. Instead of making the solutions a plug-in or a patch, our software integrates agentic AI into the operating system. This is ideal for CFOs looking to run leaner, faster, and with more control, along with real-time visibility and end-to-end, streamlined accounting processes.
Account Reconciliation Software
AI agents match transactions 10x faster, flag true exceptions, and reduce human intervention. Further agents clear intercompany mismatches using learned resolution logic. Our reconciliation software uses AI-driven agents to match transactions, flag exceptions, and apply learning-based rules for accurate, automated reconciliation. AI-driven reconciliation agent enables finance teams to achieve 99% accurate matching.
Cash Flow Forecasting Software
HighRadius Cash Flow Forecasting Software uses agentic AI to continuously refresh forecasts with live ERP and bank data. Agents detect shifts in real-time cash positions, update projections automatically, and help treasury teams manage liquidity proactively, without relying on manual inputs.
HighRadius Treasury Management Software uses agentic AI to monitor cash positions in real time, optimize allocations across accounts, and flag liquidity risks instantly. Treasury teams gain continuous visibility and can act faster, without waiting on manual updates or fragmented reports.
Financial Consolidation Software
HighRadius Financial Consolidation Software, powered by agentic AI workflows, automates real-time data collection, currency translation, and intercompany elimination. It supports top-side adjustments and streamlines the close, delivering 99% accurate eliminations and boosting consolidation efficiency by 60%.
HighRadius Financial Reporting Software, powered by AI agents, enables faster, more accurate reporting through automated drill-down analysis, dynamic variance reporting, and personalized templates. Teams achieve 80% faster reporting cycles with 95% customization to meet specific financial disclosure needs.
HighRadius stands out as a challenger by delivering practical, results-driven AI for Record-to-Report (R2R) processes. With 200+ LiveCube agents automating over 60% of close tasks and real-time anomaly detection powered by 15+ ML models, it delivers continuous close and guaranteed outcomes—cutting through the AI hype. On track for 90% automation by 2027, HighRadius is driving toward full finance autonomy.
HighRadius leverages advanced AI to detect financial anomalies with over 95% accuracy across $10.3T in annual transactions. With 7 AI patents, 20+ use cases, FreedaGPT, and LiveCube, it simplifies complex analysis through intuitive prompts. Backed by 2,700+ successful finance transformations and a robust partner ecosystem, HighRadius delivers rapid ROI and seamless ERP and R2R integration—powering the future of intelligent finance.
HighRadius is redefining treasury with AI-driven tools like LiveCube for predictive forecasting and no-code scenario building. Its Cash Management module automates bank integration, global visibility, cash positioning, target balances, and reconciliation—streamlining end-to-end treasury operations.
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Colleen Zdrojewski
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