Technology & Innovation

Machine learning in finance: SaaS use cases and benefits

Discover the use cases and benefits of machine learning in finance for SaaS CFOs. Improve reporting, forecasting, and more.

If your department relies on manual accounting, you’re almost certain to have a MUCH harder time staying ahead in the fast-paced SaaS industry. That’s why it’s crucial for SaaS CFOs to understand the uses of machine learning in finance and its benefits for accounting teams. SaaS organizations that incorporate automated tools enjoy a considerable advantage in the struggle to win their market.

In this blog, (1) we’ll explore machine learning, its role in SaaS finance, and (2) why it’s particularly important for CFOs and other accounting leaders. (3) We’ll discuss the various benefits that machine learning brings to financial management, including improved decision-making, predictive analysis, and a smoother customer experience. (4) Then we’ll wrap up with real-life use cases of machine learning in SaaS finance, such as process automation, risk management, and fraud detection. 

Understanding machine learning and AI in finance

Machine learning is the application of AI and data science that enables systems to learn and improve from experience in real time without being explicitly programmed by a person. Natural language processing (NLP) allows computers to interface with large amounts of data like a human could. 

Machine learning tools are meant to help people deal with what data scientists call “big data,” or datasets that are sufficiently large or complicated that they’re best handled by AI. SaaS finance data definitely fits that description, and CFOs can benefit from the advanced methods of analysis that this tech brings to the table. 

Machine learning algorithms can analyze large volumes of financial info from multiple data sources to identify patterns and trends, enhancing your business performance and eliminating time-consuming tasks. Beyond that, algorithmic forecast assembly eliminates the risk of manual errors and facilitates robust multi-factor, low-variance forecasts.

Your time and your employees’ time is much too valuable to spend on repetitive tasks you could outsource to computer algorithms. Artificial intelligence handles large datasets much more quickly and reliably than humans can. Let software handle the large datasets and repetitive tasks while you and your team handle the strategic heavy lifting.

How is machine learning in finance specifically applicable to SaaS accounting departments?

The role of ML in SaaS finance

SaaS companies have a unique set of accounting realities they have to navigate to grow, stay profitable, and be compliant. Automated software with deep learning and automation capabilities is perfectly suited to helping SaaS finance leaders deal with these industry-specific requirements.

Automation simplifies screening for high-credit-risk customers.

Many recurring revenue SaaS companies offer their customers the option of paying with a credit card. This is a great way to give your customers some payment flexibility but it also opens you up to financial risk.

Automated accounting software can automatically screen out high-risk customers using flexible criteria you can adjust based on your risk tolerance. Set minimally acceptable credit scores and much more.

Automated data reconciliation plugs revenue leaks. 

Revenue leakage is a notorious problem for recurring revenue companies. Machine learning in finance automates and centralizes the revenue recognition process to solve revenue leakage once and for all.

RELATED: 5 Steps to Build Your Revenue Recognition Strategy

Stop worrying about regulatory missteps and fines. 

SaaS companies that engage in recurring billing are beholden to ASC 606. It’s a complex piece of legislation that spells out highly specific rules about revenue recognition for software companies. Modern accounting suites provide a single source of truth (SSOT) that makes compliance a breeze.

Automated ASC 606 data for a SaaS company.

With automation in your department, you’ll also receive alerts whenever a regulatory change impacts your company.

Machine learning enhances customer segmentation.

Customer segmentation is an essential component of buyer data analysis for SaaS companies. Most software organizations have multiple user segments, and being able to tailor various services and perks to each one is very important.

Why is machine learning important for SaaS CFOs?

Machine learning can play a crucial role in maximizing your daily effectiveness as a SaaS CFO and lowering the probability of day-to-day problems. You have responsibilities, workflows, and results you’re directly accountable for. Machine learning can help you cut hours of tedious manual work and more easily hit your financial targets. Let’s see how.

Benefits of machine learning for SaaS CFOs

From supplying detailed process documentation to tracking and optimizing your KPIs, automation simplifies the lives of SaaS finance leaders in multiple ways.

  • Never stress over an audit: As the CFO, ensuring compliance in the event of an audit is your responsibility. Accounting software equipped with machine learning automatically saves all the crucial data you need to pass an audit: detailed transaction info and even specific drill-down data about specific customer relationships across time.
  • Machine learning improves SaaS metrics integration: Automated accounting suites dramatically enhance your visibility into your SaaS KPIs. From ARR and MRR to LTV and dozens more, modern accounting tools give SaaS finance leaders access to all their critically important metrics.
  • Automation improves cash flow management and visibility: The use of machine learning software tools can streamline cash management and boost financial visibility for SaaS CFOs. They offer laser clarity on deferred revenue timing and amounts, reduce budgeting stress, boost data visualization, and more.

That last point about cash flow is particularly important for you as a finance leader. So let’s peel back a few more layers on machine learning, cash management, and hitting your financial goals. 

3 ways machine learning improves cash management for SaaS

Effectively managing cash is your bread and butter as a finance leader. Below are three major ways machine learning helps automate and improve your cash management.

  • Budget deviation alerts help you avoid shortfalls: Budgeting is one of the most important elements of financial planning for SaaS companies. Automated software sends you alerts any time you’ve deviated from your preset plans or allocations.
  • Access detailed readouts on your deferred revenue: Deferred revenue has a considerable impact on your financial situation at any given moment. With machine learning, detailed burn-down data is always just a click away.
  • Automation shortens your DSO: Getting paid more quickly is one of the best ways to strengthen your cash management. Automation accomplishes that with automatic payment reminders, dunning emails, and, if necessary, a streamlined collections process.

Cash flow management is only one of your responsibilities, however. Let’s look at how machine learning can also enhance your financial decision-making.

How does machine learning enhance strategic decision-making for SaaS CFOs?

Modern accounting software with machine learning enables you to make more effective calls as a business leader. Its ability to detect patterns directly applies to a wide range of SaaS finance workflows. 

Predictive analysis for informed decisions

SaaS CFOs can leverage machine learning to make data-driven decisions based on accurate and real-time financial information. Modern cloud-based software can identify patterns and trends in large datasets that may not be easily apparent to humans.

Algorithmic trend analysis and pattern detection assist SaaS CFOs with:

Automated revenue data for a SaaS company.

What are some daily use cases for implementing machine learning in your department?

Exploring the use cases of machine learning in SaaS finance

Machine learning helps SaaS accounting leaders and finance teams accomplish more with less. You could almost think of AI accounting software as an “automated employee” that accurately and efficiently handles repetitive tasks in the background while you and your employees do the strategic work.

Let’s explore a few prominent examples.

Process automation & model documentation

Machine learning automation offers numerous benefits, including time-saving and reduced human error. Machine learning algorithms streamline your financial reporting and improve efficiency by automating repetitive financial tasks like customer data extraction and analysis. 

AI accounting suites also provide automatic model and process assumption documentation for foolproof compliance and peace of mind.

Risk management and fraud prevention

Machine learning algorithms are crucial in risk management and prevention within the SaaS industry. They continuously monitor financial transactions for anomalies and security threats, automating risk assessment for your department.

Machine learning models can also predict market volatility and analyze customer sentiment, allowing for better churn prediction and prevention strategies.

Financial monitoring, reporting, and forecasting

SaaS finance can be broken down into three broad task categories: monitoring for threats or updates, reporting on financial outcomes, and forecasting to predict future trends and opportunities.

Machine learning tools can help CFOs and accounting teams succeed in each area.

  • 1. Monitoring: SaaS finance leaders and their teams engage in extensive financial monitoring for various events. Budget deviations, failed payments, regulatory changes, and many other things must be watched for in case you need to take action. Modern accounting software continuously runs background screens for all that and more, alerting you to anything you need to know.
  • 2. Reporting: Cloud-based accounting tools simplify reporting for finance teams and accounting leaders by giving them access to centralized role-based dashboards. Additionally, your books are continually closed with every transaction, cutting the month-end close entirely.
  • 3. Forecasting: Algorithmic forecasts are longer-ranging, more accurate, and provide more flexibility for “if-then” scenario planning. This is a crucial element of comparing different SaaS billing models, fine-tuning your campaigns, and being able to succeed no matter what the market throws at you.
Revenue forecast data for a SaaS company.

What other benefits does SaaS accounting automation have?

Automated capitalization rundowns

Many SaaS companies raise funding rounds as they progress through their business lifecycle. This requires detailed tracking of your business valuation, funds raised, share pricing, and other essential capitalization data.

Automated software handles all of this automatically, providing updated information at the click of a button. A manual error in your capitalization info could result in serious problems at your company, harming investor trust and endangering future funding rounds.

Capitalization data for a SaaS company.

If your company takes external funding rounds, automation can help you achieve your funding objectives across your entire business lifecycle.

Enhancing Customer Relations through Personalised Services

Through its advanced algorithms, machine learning can quickly analyze vast amounts of customer data. This analysis can then be used to provide personalized recommendations and offers to your buyers, enhancing their overall experience and user satisfaction. Moreover, machine learning algorithms can identify patterns in customer behavior, helping you optimize pricing and discounts. Additionally, algorithmic chatbots and virtual assistants can offer highly personalized customer service.

Some points to consider when implementing machine learning

Automated solutions offer some incredible benefits for SaaS organizations, but there are a few things to be mindful of. One of these is the necessity of acquiring sufficient volumes of financial data that you can use to train machine learning models. You don’t have to be a large company, but you need a decent chunk of data as a starting point.

Addressing data bias is also key to achieving success with these tools. If your data is heavily biased or skewed in one direction or another, you won’t end up with profitable results because you didn’t start from an objective frame of reference.

What else do SaaS CFOs need to keep in mind when making AI a part of their department?

Addressing the skill gap

Helping your team get familiar and comfortable with these tools is one of the most important steps in implementing automation. Training and upskilling initiatives are crucial in equipping your employees with the knowledge they need to succeed with AI. If you select a reputable vendor, they should provide a dedicated customer success liaison to get your team thoroughly trained.

Continuous learning and knowledge sharing within your organization further contribute to closing the skill gap. Using your chosen vendor’s online resource center is a great way to encourage continued learning after completing the software rollout.

Managing data migration and rollout concerns

One of the most common objections to automation is based on a myth. It’s the mistaken belief that the migration and rollout processes associated with AI are difficult and also endanger the integrity of your existing datasets.

Here are the realities. Reputable software vendors will provide rollout assistance at every point in the process, ensuring everything goes as planned. Data migration concerns can be solved with a simple backup beforehand, which should be done regardless. And most vendors will provide training and support to help your employees learn these tools inside and out.

Interfacing with stakeholders effectively

The decision to embrace automated accounting will have ripple effects for every other leader at your company. Even though these impacts will be positive and increase business efficiency, you still need to make sizable efforts to tell everyone about the change well before it occurs.

This gives you a chance to assuage any doubts by educating individuals on the positive long-term benefits of automation. Schedule some time to speak with each stakeholder individually if you have the bandwidth, and explain what AI can do for their department’s performance and goals.

Setting the technological stage

Automated finance tools for SaaS companies can quickly confer some powerful advantages. But you need to take a moment to consider the different types of cloud-based accounting tools that use AI. In terms of cloud accounting suites, there are two broad types:

  • 1. Lift and shift applications: These software solutions take a precise copy of the data from your legacy apps and migrate it to the cloud. Once it’s there, you can continue working as you normally would. Although this might sound ideal for a busy SaaS company, the lift and shift approach is known to be unreliable. Even though these tools offer bits and pieces of AI and algorithmic functionality, their limited integration capabilities often keep that in the realm of theory rather than practice.
  • 2. Cloud-native planning tools: Cloud-native planning software, on the other hand, is built specifically for the cloud. It offers end-to-end encryption for your valuable data, whereas lift and shift apps are known to be a security hazard. Built-in integration with AI and machine learning means that these aspects of the software operate as expected rather than the unpredictable performance of the lift and shift approach.

However, that’s not the only thing you need to be mindful of when selecting an automated accounting suite.

Prioritizing built-in scalability

Scaling effectively should be a top priority for any SaaS CFO. Growth is always worth aiming for–we certainly won’t fight you on that. But you should ensure you have the underlying software flexibility needed to sustain growth once you achieve it. 

As your company expands, the amount and complexity of the customer data you take in will increase exponentially. This makes it crucial to purchase an automated planning tool that can effortlessly scale up to meet the financial demands that come with a larger subscriber base.

Just as importantly, if times get tough and you need to scale back, your software should be able to do that without any problems.

Step into the future of SaaS accounting

The limitations of legacy accounting tools are becoming increasingly apparent to SaaS CFOs and accounting leaders at many companies. The benefits of machine learning are difficult to ignore–from streamlined reporting and financial monitoring to more robust forecasts, better data security, and more. Automation opens up levels of operational efficiency that can’t be obtained manually.

At the same time, though, SaaS CFOs need to keep their eyes open when embracing this powerful technology. Transitioning to automation isn’t rocket science, but you need to follow a strategically phased plan to guarantee success. Our recent ebook helps CFOs better understand these tools–what goes into a smooth migration, and what questions do you need to ask to ensure success with automated planning tools?

You can read it here.

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