5 Transformative Use Cases for Agentic AI in Finance 

Introduction

In every organization, the finance department constantly faces operational challenges. Think about getting a contract to revenue: credit teams get stuck waiting for references. Purchase Orders often have errors. Unexpected deductions appear, and validating them is slow. Meanwhile, collections teams chase payments, unsure of whom to prioritize.

This situation reflects a fragmented and reactive operational reality. Even with existing automation, a significant portion of work remains manual. Knowledge often resides in silos, and teams frequently switch between disparate systems. This ultimately creates a substantial barrier, impacting efficiency and hindering effective strategic planning.

Thankfully, agentic AI in finance is changing this dynamic as it significantly surpasses simple automation capabilities. It helps finance teams simplify daily operations by eliminating repetitive tasks and removing bottlenecks across the Order-to-Cash process.

In this blog, let’s explore how agentic AI goes beyond traditional automation to tackle long-standing inefficiencies across various finance departments.

Understanding the Agentic AI Framework

Agentic AI describes autonomous agents powered by advanced language models and reasoning engines. These agents can self-direct, reason, plan, act, and learn, coordinating to tackle real-world problems without waiting for explicit human instructions. Unlike traditional automation, which follows rigid instructions, agentic AI operates with autonomy. It breaks down complex problems, makes real-time decisions, and takes actions by accessing tools, APIs, or databases as needed. This allows businesses to move from static workflows to intelligent, responsive systems.

At the core of agentic AI framework is an orchestration layer that connects the model, memory, tools, and task logic. Depending on the use case, agentic AI may act as a single self-directed agent or coordinate multiple agents—each with a defined role—to accomplish more complex workflows. The result is a system that can not only execute tasks but also optimize them over time, making it ideal for dynamic, decision-heavy processes in finance and operations.

The Gaps in Traditional Finance Processes

While traditional finance systems have served businesses for decades, they were built for a slower, less complex world. Today’s fast-moving environment exposes their limitations.

1. Data Silos & Legacy Systems:
Finance teams often operate on fragmented data across old systems and spreadsheets. This hampers real-time decision-making and cohesive workflows.

2. Manual Workflows:
Tasks like month-end closing, loan approvals, and compliance reviews are labor-intensive, slow, and prone to human error .

3. Lack of Proactive Risk Management:
Current systems rely on periodic reviews. They fail to continuously monitor borrower behavior, liquidity risks, or compliance breaches in real time .

4. Limited Flexibility & Scalability:
Static rule-based systems can’t adapt to new scenarios or legal changes quickly. Updating policy often requires manual intervention .

5. Governance & Transparency Challenges:
Automated processes without clear audit trails face regulatory and ethical scrutiny. Financial systems need explainable decisions and reliable risk models .

6. Talent & Infrastructure Gaps:
Deploying intelligent systems requires cross-functional expertise and modern infrastructure—skills and resources many institutions lack .

5 ways Agentic AI is Transforming Finance

Here are five transformative use cases where Agentic AI is truly making a difference in the finance landscape:

1. Smart Exception Handling in Cash Application: Tackling the Unidentifiable

Even with high automation rates in cash applications, there’s always a percentage of payments—say, 10% to 20%—where remittance information is missing, or the payment details simply don’t match up. These exceptions typically require human intervention, and analysts spend significant time chasing down information or manually reconciling discrepancies. This can delay cash posting and impact financial visibility.

Cash Application software powered by agentic AI is a game-changer here. It learns past payment patterns to predict missing remittances, offering suggestions to analysts or even automating email correspondence to customers requesting the missing information. It can automatically identify the most probable customer for a payment or match the most likely remittance to an unmatched payment, flagging these for user review. Furthermore, it automatically assigns each exception payment to an analyst’s work queue, optimizing exception clearing.

The Business Outcomes of Using Agentic AI in Cash Application

• Increase in remittance matching rates
  AI learns from historical patterns to match more payments automatically, reducing manual clean-up.
  
• Savings in lockbox fees
  Fewer payments routed for manual key-in lowers bank processing charges significantly.
  
• Drop in unapplied cash
  Smarter matching clears payments faster, giving treasury earlier access to cash.
  
• Same-day cash posting becomes the norm
  AI routes and resolves exceptions in real time—no more multi-day backlogs.
  

Real Impact: How a global CPG leader Transformed Exception Handling

Danone, a global CPG powerhouse, faced mounting challenges in its cash application process:

•  250+ hours/month spent on applying e-payments and manually capturing remittance data from 25+ customer portals
  
•  Manual mapping of 4,000+ customer deduction reason codes to ERP-specific codes
  
• Limited bandwidth to identify and recover invalid deductions, leading to higher write-offs and delayed cash posting

Delays in cash posting were slowing everything—from collections to cash flow insights. Agentic AI not only cleared the backlog but redefined their entire approach to payment processing. It helped the organization recover $20M from in invalid decudtions. Additionally automatic write off of low dollar value-valid deductions based on business rules, resulted in imporoved productivity of the team. Thus, a smarter approach to exception handling changed everything—with results worth noting for every enterprise.

2. Intelligent Collections Prioritization & Outreach: Smarter, Not Harder, Collections

Collections departments face a constant challenge: managing a high volume of accounts, manually creating call lists, prioritizing efforts, and then actually reaching out to customers effectively. This takes their precious time, delaying outreach and impacting productivity.

Agentic AI brings much-needed intelligence to the collections process. For example, AI-Based Worklist Prioritization module in collection software, can evaluate customer portfolios across more than 20 parameters, like past payment behavior and credit risk, to identify the highest correlation parameters to past due accounts. This creates a stack-ranked list of customers for each collector, ensuring they focus on the riskiest accounts first. 

There’s  more, automated agents for dunning emails can intelligently send reminders at optimal times, ensuring they’re opened and actioned. For more personal outreach, an In-App Outbound Call Agent can create talking points, auto-pull contact information, and even transcribe calls in real-time, drastically increasing the number of calls a collector can make per day. This focus on smart prioritization and automated outreach means better coverage and a significant reduction in past-due receivables.

Business Outcomes of Using Agentic AI in Collections

• Higher past-due recovery rates by focusing collector effort on the riskiest accounts
  
• More accounts touched per collector per day through intelligent prioritization and automated outreach
  
•  Touchless collections workflows with dunning emails and faxes sent automatically based on payment stage
  
• Shorter collection cycles, driven by timely, data-backed actions and fewer manual follow-ups
  

Real Impact: How the second largest global confectionery brand Transformed Collections Efficiency

Ferrero, a global confectionery brand, was struggling to scale its collections process in the face of increasing account volumes:

•  Difficulty tracking critical and past-due accounts in a timely, structured way
  
• Longer collections cycles due to manual and time-intensive dunning emails and follow-ups
  
• Lack of visibility into customer profiles caused by siloed tools and fragmented data across teams
  
• Collectors were losing time on low-value tasks and working reactively—with little control over prioritization or outreach strategy. 

Agentic AI changed the equation. By leveraging AI-driven worklists, automated dunning, and more, Ferrero reimagined how collections should run in a modern O2C setup. The company experienced a 28% decrease in DSO while maintaining higher productivity.  What followed was higher output, faster follow-ups, and better recovery—all without adding headcount.

3. Proactive Dispute Resolution & Prevention: Cutting Down on Deductions and Disagreements

Deductions, the amounts customers subtract before paying an invoice, are a significant pain point, especially in industries like CPG or manufacturing. Researching and validating these deductions, aggregating supporting documents, and resolving disputes can be an incredibly time-consuming, manual process. The challenge lies in moving from a reactive stance—addressing disputes after they arise—to a proactive one.

Agentic AI in deductions flips that reactive process. It offers a robust solution here, starting with Dispute Prevention agents that perform two-way or three-way matching between invoice and PO data, flagging potential disputes before they even become an issue. It even identifies root causes of disputes, allowing for preventative measures. When disputes do arise, AI powered dispute resolution software can help import disputes from various sources, auto-create workflows, and streamline collaboration across departments, ensuring faster resolution. It identifies patterns in root causes and automates resolution workflows with built-in deduction coding, rules libraries, and claims backup retrieval from portals or inboxes.

 Business Outcomes of Using Agentic AI in Deductions Management

• Faster deduction resolution cycles with automated case creation and routing
  
• Automated claim backup retrieval from portals and inboxes—eliminating hours of manual work
  
•  Fewer invalid deductions missed thanks to early dispute prevention and root-cause insights
  
• Automatic coding of deductions based on a pre-trained library of customer-specific rules
  
• Reduction in bad debt and write-offs by addressing revenue leakage at the source

Real Impact: How the largest confectionery Company Took Control of Deductions

Hershey’s, one of the world’s largest confectionery companies, faced persistent challenges managing high volumes of deductions from major retail partners

• Delays in resolving and recovering deductions due to manual effort spent on researching claim validity, matching POs, and locating backup documentation across systems
  
• Inefficient collaboration across credit, sales, and deductions teams, often slowed by email threads, unclear ownership, and delayed approvals
  
• Limited visibility into deduction status and root causes caused by siloed systems—forcing analysts to toggle between ERPs, spreadsheets, and inboxes to piece together information

Hershey’s needed a smarter way to prevent disputes, accelerate resolution, and cut down the cost of chasing deductions. Agentic AI changed that. With automated backup collection, pre-trained deduction coding, and early dispute prevention, Hershey’s simplified resolution and stopped revenue leakage before it started. They moved faster, stopped more invalid claims, and recovered more revenue with 70% reduction in manual efforts needed.

4. Streamlined Electronic Invoicing: Ensuring Compliance and Swift Delivery

The invoicing process, while seemingly straightforward, is fraught with its own complexities. Businesses need to ensure timely and accurate invoice delivery, comply with evolving global e-invoicing mandates, and often navigate a maze of customer AP portals, each with its own requirements. Manually uploading thousands of invoices to various portals is far from an ideal use of a billing analyst’s time.

Agentic AI streamlines this crucial step. AI powered E-Invoicing solutions can automatically identify invoice destination systems based on geography and compliance requirements, generating and delivering invoices through the preferred channels. It also automates the delivery of invoices to major AP portals via integrated Robotic Process Automation (RPA), handling complex login details and tracking acceptance statuses. Beyond formal portals, an Email Invoice Delivery Agent ensures invoices are sent with smart payment links, tracking delivery status in real-time. 

Business Outcomes of Using Agentic AI in Invoicing

Companies that adopt Agentic AI for invoicing typically experience:

• 100% automated invoice delivery across email, AP portals, and government platforms—eliminating manual uploads
  
• Built-in compliance with global e-invoicing mandates, reducing rejection rates and delays
  
• Reduction in analyst time spent on portal logins, formatting, and tracking delivery status
  
• Faster payments with embedded payment links, making it easy for customers to pay the moment they receive an invoice

Real Impact: How A Fast Growing Staffing and Consulting firm Automated Electronic Invoicing at Scale

Addison Group, a staffing and consulting firm, faced critical invoicing bottlenecks that slowed down billing and impacted collections:

•  Multiple ledgers across business units led to hours of analyst time lost manually generating and sending invoices across systems
  
• High invoice volume made it difficult to prioritize and track invoice delivery, delaying follow-ups and slowing collections
  
• No integrated payment portal, limiting customers’ ability to pay invoices instantly via credit or debit cards

Agentic AI changed that. Their invoicing process became smart—learning destinations by geography, complying automatically with tax mandates, and plugging invoices directly into systems like SAP Ariba, Coupa, Taulia, or email—with real-time delivery tracking. The company achieved 2x growth in 2 years with the help of AI automation. Analysts no longer fought portals, passwords, or formats. And nothing fell through the cracks.

5. Continuous Credit Monitoring & Risk Scoring

Credit reviews shouldn’t happen quarterly—they should happen constantly. Yet most teams still rely on static evaluations and outdated risk flags. That leaves you exposed when a customer’s financial health changes between reviews—and by then, it’s too late.

Credit management solution powered by agentic AI flips the model. Instead of reviewing accounts on a schedule, it creates a live credit profile for every customer—pulling from real-time financials, trade behaviors, and bureau scores. Risk scores are continuously updated. If something changes, the system immediately flags it and can suggest or apply new terms—tightening payment windows, lowering release limits, or escalating for manual review.

This is how credit teams move from reactive clean-up to active risk control—guarding working capital before problems arise. By replacing periodic credit reviews with continuous credit monitoring using AI, finance teams can cut delays, build faster approvals and make smarter decisions.

Business Outcomes of Using Agentic AI in Credit Management

• Reduction in time taken for periodic credit reviews with always-on monitoring
  
• Faster customer onboarding by eliminating manual document checks and validations
  
• Increase in credit limit reviews performed—without expanding team capacity
  
• Improved risk coverage with auto-alerts and suggested actions based on dynamic score changes
  
• Lower exposure and default risk through proactive limit management and real-time visibility

Real Impact: How a Global Manufacturing Leader Achieved 89% Faster Credit Reviews Using Agentic AI

Chevron Phillips Chemical, a global manufacturing leader, faced growing complexity in managing credit across diverse customer portfolios:

• Delays in customer onboarding due to manual financial data collection and fragmented internal processes
  
• No structured way to prioritize customers by credit risk, leading to equal effort on low- and high-risk accounts
  
• Lack of real-time access to customer credit data, making it difficult to act quickly on changing risk conditions
  
• Limited visibility into joint venture customers’ financials, creating roadblocks in reviewing and setting credit terms
  

With Agentic AI, the credit team moved from periodic, reactive reviews to continuous, risk-based credit management. Real-time data aggregation, dynamic score updates, and automated workflows helped them accelerate onboarding and mitigate exposure faster than ever. As a result, the organization  achieved 61% faster customer onboarding. Credit management that used to be manual, slow, and reactive for Chevron Phillips, is now continuous, intelligent, and ready to scale.

Bottom Line

Agentic AI in finance isn’t just another wave of automation—it’s a complete shift toward orchestrated workflows that think, act, and learn in real time. By handling routine tasks, optimizing decisions, and flagging irregularities autonomously, it frees teams to focus on strategy, collaboration, and higher-impact work. The result is not simply efficiency, but an intelligent, proactive finance operation—one that adapts and evolves alongside business needs

If your workflows still lean on spreadsheets, dated reminders, or reactive fixes, this reveals a smarter route forward. Agentic AI offers system-level efficiency, resilience, and a move into true financial strategy—automating the mundane and freeing your team to steer the ship.