Maintaining stable finances is a core concern of almost every business, regardless of the size or type, because — unless you happen to be independently wealthy and willing to throw good money after bad — you can’t keep operating once your funds run dry. But it’s often a complicated matter. Payments big and small stack up, economic circumstances shift, and revenue varies. Even if you’re smart and careful, you can still have great difficulty.
In recent years, though, the field of big data has moved from an interesting consideration to an influential practical reality. Across industries, businesses are using rich data analysis to achieve remarkable things: yielding valuable insights and prompting operational improvements. It helps us work smarter, not harder. Can it be used to shore up financial security as well?
The simple answer is “yes”. If implemented correctly, algorithms can absolutely produce a seismic shift in how businesses handle their money. In fact, given time, they surely will. Let’s take a closer look at the fintech convenience that’s waiting over the horizon:
The practical demands of cash flow calculation
In essence, cash flow is the amount of money your business brings in during a particular period. Stacked up against the amount of money it spends during that same period. Companies typically look monthly, but it could also be weekly (depending on the circumstances — the regularity of the payments, etc.). If you bring in more than goes out, you have a positive cash flow.
Despite the immense importance of cash flow, it’s often overlooked and/or misunderstood. Companies think that profits is what they need to prioritize. Not necessarily— at least, not to the extent they believe. Profit matters in the long run, but cash flow matters all the time.
What’s the difference? Profit is based on completed transactions — when everything has concluded, did you make money on a deal? But cash flow doesn’t wait for completion. It cares about the money you’ve secured. If you spend heavily on launching deals that will yield major profits in six months, you can run out of money within two months, and be unable to continue.
What machine learning brings to the table
There are various ways to calculate cash flow. But so many elements go into it that it can be a logistical pain to deal with. Some companies hire CFOs (chief financial officers) and task them with getting it all figured out, but good CFOs are costly. There are cash flow formulas you can try, and they can be reasonably simple, but it still takes time to get everything lined up when you’re working at an enterprise level.
This is where machine learning has a lot to offer. Machine learning (or ML) is the simplest form of AI technology (tech that seeks to mimic human capabilities). It uses algorithms designed to be adaptive. That means that an ML algorithm does more than rigidly follow one set routine — it draws from available data to decide how best to proceed.
This makes ML exceptionally strong at carrying out repetitive tasks while iteratively yielding better results, which is exactly what you want from a fintech system. You don’t just want something to work in the background and tell you how much money you’re making you want it to help you do something with that information.
Convenient integration in modern APIs
Modern payments can get extremely fragmented. This is largely due to several factors. The popularity of SaaS solutions. The commonality of micro-transactions. And the range of digital payment gateways and wallets further complicating matters. It’s not simple as it used to be to keep track of everything. Doing everything manually has become a messy matter of leaping from system to system while hoping that you don’t miss anything.
The existence of modern APIs, however, means that automation is extremely accessible. Software solutions are designed for effective communication. Because developers know that users rely on complex ecosystems. And they will always prefer to invest in solutions that they know will slot neatly into those ecosystems.
And even when they won’t get along natively, the convenience of tools such as Zapier or IFTTT makes it fairly easy to link systems together. For a cash flow algorithm, this makes the data-acquisition step totally straightforward: hook it up to every software service you use, and it can rapidly hunt down every invoice and every scheduled payment. It can even look through client communications to find identified deadlines.
Forecasting and optimization
So, as noted, machine learning software is perfectly capable of digging through your data and giving you an accurate and up-to-date cash flow assessment. That’s useful in itself, certainly, because it’s only the beginning. What we’re going to see much more of in the near future is a rich combination of forecasting and optimization.
Solutions such as Fluidly and Dryrun already offer ML-driven cash flow forecasting (though it’s nowhere near as extensive as it will be in a few years), so it’s not like this is purely hypothetical. Businesses can use these tools to predict their future cash flow, and glean potential improvements — for instance, pushing one set of payments back and moving another set forward might yield much greater operational stability.
Eventually, there will be fintech suites that bring cash flow management as close to complete automation as you’re willing to get. Once you’ve established the basic parameters and restrictions, you’ll be able to give the suite total authority over a range of matters. Including the ability to rearrange your payments, chase missed invoices, and even negotiate rates (likely with other ML systems). This will radically change how businesses handle their finances. And make it hugely easier for the average start-up to achieve growth with limited resources.
Over the course of the coming years, we’re going to see the foundations of the business world completely overhauled. By the power of ML algorithms drawing from big data. And fintech is an arena that’s going to prove particularly influential. Anyone who wants their business to get more competitive would do well to take advantage as soon as possible.