Corporate finance operations oversee budget planning, transaction management, bookkeeping, and maintaining compliance. These functions are crucial for a business’s efficient capital allocation for survival and growth.
Due to their importance, corporate finance operations workflow tasks demand keen attention to detail, collaboration, and alignment. As a result, teams have to deal with various administrative action items that cost them a chunk of their day.
For instance, coordinating with vendors and partners while managing invoices and processing payments through email can be tedious. However, the task itself is quite simple, as it involves accurately updating the stakeholders.
However, performing it demands that finance teams in corporates juggle between multiple tools to fetch the latest updates before engaging in communication.
Some other examples of such repetitive tasks include supporting audits, preparing quarterly reports, and drafting invoices.
To improve productivity while maintaining the quality of work, corporate finance operations teams can leverage artificial intelligence (AI) solutions.
In this article, let’s look at five ways AI tech can be used to enhance corporate finance operations.
1. Leverage Agentic Automation
Automation is not new to current finance teams. Corporations have been creating rule-based automation workflows. Various activities, such as verifying vendor details, sending follow-up messages, or generating timely reports, are largely done through automation.
However, static automation has its limitations.
It can’t respond to dynamic requests. In fact, it may malfunction or cause damage if they encounter anything slightly outside their barriers.
This poses a critical challenge because of the evolving nature of corporate finance operations.
Process change, new tools get adopted, philosophies evolve, or the financial regulations receive new amendments. Tackling these translates to tweaking the static automation processes as much as required.
A much more autonomous solution is to leverage Agentic Artificial Intelligence (Agentic AI). These solutions have the ability to make context-aware decisions within the financial workflows.
Consider verifying vendor details in an invoice. In traditional automation, teams will have to upload the invoice in a specified format. Then, it will scan the details and check them against the records.
However, with agentic AI, corporate financiers can upload the invoices that follow any template to fetch the vendor details accurately. This means the professionals can directly paste screenshots to ensure the payment receipts are valid.
Setting them up is also easier than establishing a trigger-based automation rule book. Teams can simply tell the AI their objective and preferences, and the software will figure out the process.
Additionally, these applications consider the present circumstances rather than a strict set of instructions, making them agile to changes.
2. Optimize Accounts Payable and Receivable
Accounts Payable (AP) handles a company’s outgoing payments to vendors and partners.
Accounts Receivable (AR) keeps track of payments from clients or customers.
Both of them ensure a business or enterprise has enough capital to efficiently run daily operations, pay overhead costs, and maintain healthy margins.
The corresponding workflows within AP and AR include administrative tasks, such as entering invoice details into ERP, addressing document discrepancies, and verifying receipts. These action items can cost corporate finance many hours.
Moreover, when they are done by humans, the chances of oversights and errors increase, affecting efficiency further.
AI can aid corporate financiers by tracking anomalies across payments, answering routine questions through chatbots, and flagging potential compliance issues.
Furthermore, teams can give these smart software complex instructions, such as “inform the vendors about invoice detail mismatch through email,” to accelerate the overall workflow further.
To increase the overall impact of AI in a corporate finance environment, professionals can combine it with additional tools like a free invoice generator , data logger, and workflow tracker.
This will enable organizations to complete AP and AR transactions faster with fewer errors to elevate vendor/partner or client/customer relationships. Consequently, teams can focus more on strategic initiatives that drive business growth.
It is crucial to adopt a phased implementation approach toward AI adoption for enhancing AP and AR processes. First, organizations should integrate it with existing ERP or accounting systems and run a few experiments before making it mainstream within the company.
3. Enhance Budget Forecasting Accuracy
Budget forecasting estimates the future income of a business in a specific period. It is done by analyzing past sales data and market trends. The accuracy of the prediction depends on the data’s legitimacy and the analysis method.
Most modern forecasts are analyzed through proprietary machine learning (ML) algorithms that are created by experienced data scientists and engineers. Hence, in reality, the key element that determines a corporate finance operations team’s budget forecast is the data’s legitimacy .
The challenge with traditional analytics processes is that they can only leverage static datasets.
Consequently, they can’t offer insights based on real-time data. This limitation can make any business vulnerable, as the corporate financial predictions don’t take the recent potential vulnerabilities into consideration.
The current generation of AI models navigates this roadblock effectively. These tools ingest vast datasets in real-time, including internal records, market trends, seasonal patterns, and even macroeconomic indicators.
This gives corporate finance departments comprehensive insights into their income estimations.
The added accuracy is crucial for avoiding risks, capitalizing on opportunities, and preparing for upcoming challenges effectively. As a result, the finance teams can make decisions confidently while decreasing reliance on assumptions and gut feelings.
4. Strengthen Fraud Detection and Risk Management
Corporate finance teams must shield themselves from different cyber threats, such as phishing attacks, identity spoofing, invoice fraud, and insider threats. Evolving workflows and tech stacks increase the vulnerabilities further.
Legacy security systems leveraged by corporations used to be effective. They fall short of tracking these modern data breach attempts, let alone predicting vulnerabilities before they escalate.
AI-enabled security solutions address this limitation of corporate finance operation processes. These platforms analyze massive volumes of financial activities in real-time. This includes internal action items and external engagements from various stakeholders.
Consequently, teams can spot anomalies immediately rather than after they start affecting operational resilience.
Another advantage of using AI for detecting fraud and managing risks in corporate finances is model evolution. ML algorithms keep improving based on what they encounter in the real world, securing the financial systems further.
Finance departments in corporates can remain stress-free as masked defects, such as duplicate claims or inflated charges, will be detected efficiently. Additionally, AI can keep an eye on liquidity risks, credit exposures, and supplier reliability to minimize risk and decrease instances of fraud further.
To improve the overall efficacy of this approach, businesses should integrate their AI-driven safety platforms into their current tools, such as the CRM and accounting systems. Moreover, auditing the model regularly to reduce bias and improve accuracy is crucial.
5. Streamline Regulatory Compliance
Apart from ensuring the financial activities, such as payments, are done correctly, corporate teams must establish their legality. This is pivotal for protecting the interests of all stakeholders and elevating the legitimacy of the business.
Compliant financial operations also smoothen tax filings and make audit trails more reliable, increasing transparency across the organization.
On the other hand, non-compliance, or even misunderstandings associated with it, can lead to financial penalties, reputational damage, and legal consequences.
A challenge corporate finance departments encounter while maintaining compliance is keeping up with the evolving regulations. Changing laws affect internal workflows and external vendor requirements.
Tweaking the associated verification and approval processes from time to time can lead to halted operations while increasing the chances of oversights.
AI-powered natural language processing (NLP) tools can help with this by analyzing the latest legal texts and suggesting simple yet effective improvements for the existing compliance workflows.
If corporate professionals in the finance department want to adopt a more personal approach, they can use AI to just flag the amendments to make the strategic adjustments on their own.
This approach can help organizations move quickly without pausing current operations or overhauling entire processes. Moreover, communicating the potential changes in the regulatory compliance workflow can be easily summarized for record-keeping and updating stakeholders.
Wrapping Up
Corporations must establish efficient finance operations to improve strategic planning, compliance, and financial stability. Additionally, teams must retain agility and workflow resilience to keep up with the evolutions.
It can be difficult to do it all through manual efforts. Finance professionals in corporates can get stuck in endless administrative tasks before diving into meaningful deep work.
AI offers an effective way out by bringing advanced solutions into corporate finance operations.
First, agentic AI platforms can automate complex but repetitive action items effectively. These tools can tackle dynamic challenges, such as invoice verification, report creation, and answering queries.
Then, AI can help streamline accounts payable and receivable workflows by streamlining data entry, invoice management, and payment tracking. Additionally, predictive models can generate accurate budget forecasts, increasing teams’ confidence in financial decisions.
Finally, AI-powered solutions also secure financial workflows by spotting anomalies before they escalate into issues. Another way they protect organizations is by enhancing regulatory compliance.
These models track changing laws and policies around corporate finance to help improve internal processes. This is essential for upholding stakeholders’ trust while avoiding legal troubles and reputational damage.
