Financial process automation: 7 use cases for SaaS

Are you tired of manual financial processes that suck up too much time, effort, and cash? Automation can solve many of the traditional headaches associated with manual bookkeeping and financial workflows. In this blog post, we discuss how artificial intelligence (AI) and machine learning (ML) work together to automate financial processes for SaaS companies.

We’ll begin by comparing traditional financial processes with automated ones, highlighting the advantages and challenges of transitioning from one to the other. Then, we’ll dive into real-world applications of AI and ML in SaaS financial process automation, providing examples of how these technologies can optimize your forecast modeling, enhance financial reporting, streamline accounts payable, and more.

Understanding AI and ML in financial process automation

AI and ML revolutionize financial processes by improving task performance efficiency and accuracy. By automating repetitive tasks, they free up time for high-level strategizing that would’ve otherwise been spent on low-level manual activities.

AI and ML are transforming financial processes across the board, benefiting large public companies and small SaaS startups alike. Let’s look at what they are and how they relate.

The intersection of AI and ML

In computer science, AI refers to software systems that can perform tasks requiring human intelligence. AI uses natural language processing and speech recognition to interface with data. Machine learning, a subset of AI, enables systems to use training data to engage in supervised learning or unsupervised learning.

This combination allows accounting automation software to quickly analyze large amounts of financial data, revealing insights about:

• Pricing strategies and revenue trends
• Financial threats and opportunities
• Patterns in your SaaS metrics

These tools improve performance and streamline cash flow through task automation. Let’s learn more. 

The role of AI and ML in automating financial processes

AI and ML are instrumental in automating financial tasks and processes for SaaS CFOs and finance teams. By leveraging AI, you can eliminate manual data entry and processing, streamline your financial operations, and enhance the day-to-day workflows in your department. 

Moreover, ML algorithms can detect anomalies and inconsistencies in your financial data, ensuring accuracy, security, and reliability.

Some examples of process automation in SaaS finance

Once an accounting automation suite has been trained on your company’s datasets, it can efficiently handle all sorts of financial processes that previously required a human employee. In addition to creating invoices and matching them with customers at the appropriate time, AI and ML can handle other complex processes such as:

• Optimizing your accounts receivable process

• Preparing your balance sheet and other financial documents
• Enhancing revenue recognition accuracy, and much more

We’ll get into more detail about specific use cases in a moment. First, though, we need to see how legacy accounting processes compare with automated workflows–we’ll also share tips for smoothing out the transition to automation at your company.

Comparing traditional financial processes with automation

Automating your financial processes offers many advantages compared to the traditional methods found in legacy accounting departments. By getting rid of manual data entry, automated processes eliminate employee errors, ranging from revenue recognition problems to invoice mistakes and more.

But that’s not the end of the story. In addition to automating discreet financial processes, AI accounting suites automate the entire data-sharing process for software companies. Organizations that use legacy accounting almost always silo their data. “Data siloing” is a term in data science that refers to the practice of independently managing information in different departments of a company. It’s known to be inefficient and runs the risk of creating inaccuracies in your data points and metrics.

Accounting automation introduces a single source of truth (SSOT) that gives every department access to real-time financial data whenever it’s needed. 

Challenges in transitioning your accounting department to automated processes

Implementing automation in your financial processes isn’t rocket science, but it does require some strategic forethought. Below are a few common challenges SaaS CFOs encounter when transitioning their companies to automated accounting.

Incompatibilities with your existing tech stack or tools

Automated software can’t effectively map onto outdated legacy tech. Optimized performance requires a full migration. Although some companies try to combine legacy and cloud tech through “lift and shift” apps, these are known to be unreliable and lack full AI functionality.

Employee resistance to change

One of the best ways to address this pain point with your employees is to let them know they’ll still be needed. The purpose of these tools isn’t to replace employees but to increase their strategic bandwidth by offloading repetitive processes.

Lack of specialized knowledge or competence

If you select a reputable vendor of AI accounting software, they’ll provide a customer success liaison to get your department up and running. Training sessions will be provided at times that fit your team’s schedule, and most companies will also have a resource center with educational and training materials for supplemental learning.

By addressing these challenges, organizations can successfully transition from traditional to automated processes. Now we’ll explore seven high-impact use cases of AI and ML for automating financial processes at your SaaS company.

7 applications of AI and ML in SaaS financial process automation

AI and ML can streamline an impressive range of financial processes for SaaS companies. These real-world use cases enhance efficiency, accuracy, and productivity. They help CFOs simplify operations and drive long-term success.

1. Optimized forecast modeling and documentation

Automation software streamlines forecasting processes, saving time and eliminating errors. Just plug in your starting data, and AI takes care of the rest.

Compared to legacy forecasting processes, automation provides superior performance in several important ways:

• Dynamic scenario planning: Traditionally, financial modeling was a single-instance process. So if external conditions were to change, a company’s models would need to be manually updated to reflect that. ML-based models are dynamic–instead of planning for one scenario and one set of financial assumptions with each model, ML models can dynamically shift to reflect the changes occurring around you.
• Longer-range forecasts with lower variance: Forecast length and accuracy are two of the most essential ingredients for lasting financial success. Automated forecasting produces more accurate results than spreadsheet-based forecasts and allows you to see further into the future.

• Protection against employee-based bottlenecks: When organizations rely on employees to create financial forecasts, they expose themselves to potential business interruptions. What if the person who oversees modeling leaves the company or goes on maternity leave? Even a short-term disruption in your forecasting efforts can have massive impacts on your company. AI prevents downtime in your essential processes.

There’s one more point to consider about forecasting. AI accounting suites automate your company’s process and assumption documentation. If you use ML for financial modeling, you’re legally required to keep detailed records of the steps you take in creating your models. Having that info on tap at a moment’s notice can make all the difference in an audit.

2. Simplified ASC 606 upkeep

Compliance management is another area where automation makes a sizable impact for SaaS companies. ASC 606 can be complex and time-consuming to manage manually, but companies can significantly simplify the process with automation.

There’s a lot that goes into following ASC 606, but it can be summed up in 5 primary steps:

1. Identify all contracts you have in place with a customer.
2. Spell out the precise service obligations you owe the customer.
3. Assign a specific transaction price to each of those services.
4. Deliver those services across the contract lifecycle.
5. Recognize revenue as you fulfill its attached performance obligations.

When you keep track of it manually, this process involves a multitude of cross-checking and reconciliations between entries. Over time, this results in leaked revenue–cash flow that your company can’t officially recognize because it falls outside the requirements of ASC 606. Automation permanently ends revenue leakage by giving departments access to an SSOT and using ML to carry out the recognition process.

3. Integrated SaaS metrics

Your SaaS metrics give you an objective touchpoint for gauging your company’s financial success. They’re also the strategic backbone of pricing strategy formulation, FP&A, and more. Automated accounting provides continuous real-time insights into all your vital metrics.

This allows you to instantly spot and capitalize on usage trends, maximizing your profit potential through early pattern detection. With automation in your department, you’ll have access to dozens of customer success metrics and revenue metrics that can keep logo churn to a bare minimum.

4. Streamlined security and fraud detection

Data security is naturally top of mind for SaaS CFOs. Hefty fines are imposed on companies that allow data leaks to occur–and the more customers you have, the more you stand to lose. That’s where ML comes in. Once you’ve trained automated accounting software on your company’s data, you can set up customized risk parameters. Your AI software will then engage in continuous pattern recognition processes that trigger an alert if anything suspicious is detected.

In addition to that, your data will be fully encrypted for total peace of mind.

5. E-invoicing

Late payments and manual invoice mistakes can significantly lengthen your cash cycle, negatively impacting every area of your business. E-invoicing solves those problems by:

• Eliminating manual errors: Going back to correct a manual invoice error is time-consuming, expensive, and reputationally damaging. Automation gets to the heart of the problem by removing the manual component.
• Making payments simple: E-invoices include a “Pay Now” button, which allows customers to pay on receipt. This simplifies the process for your users and helps get you paid more quickly.

Automated invoicing can also give you more peace of mind by increasing the predictability of your cash flow.

6. Continuous close

One of the most useful aspects of automation is its ability to completely cut the close. Similarly to many other workflows we’ve covered in this post, your books are continuously closed thanks to automatic background processes.

This takes a considerable workload off your department, freeing you and your team up for more strategically meaningful work.

7. Centralized capitalization management

Many SaaS companies take venture capital funding to expand more quickly or better execute their company’s vision. If you handle the capitalization process manually, you could run into a range of problems:

• Inaccurate capitalization data
• Valuation difficulties
• Missed funding goals or obligations

Cloud-based capitalization management can help you hit your funding objectives at every stage of your company’s journey. [Funding objectives article link]

Are you asking the right questions?

Financial process automation with AI and ML is usually the right move for large and small businesses alike. The SaaS industry has reached a point where, for many organizations, legacy accounting just isn’t practical. But even if you know that switching to automation is the best choice for your company, do you know how to guarantee a seamless and secure migration to the cloud?

It’s not overly difficult, but it does require asking a specific set of questions about your SaaS business. Our recent ebook covers what those are and can help guide you through the steps you should follow for a painless migration to AI. You can check it out here.