Last editedJun 20212 min read
Every business needs to stay ahead of the curve, and machine learning is key to that. Many business processes have already been automated, but some are trickier than others. Learn more about the relevance of machine learning for accountancy, right here. First off, what is machine learning?
Machine learning definition
Machine learning is a form of artificial intelligence. It refers to the concept that machines can learn new things based on the information they process. A computer capable of machine learning will recognise patterns and may be able to generate new algorithms based on these patterns. Machine learning systems will not need explicit instructions to use these algorithms, as they will learn them on their own.
Many of the services you use in day to day life rely on machine learning – movie streaming services, for example, learn the patterns of your behaviour to make suitable recommendations on your home page. It’s also the reason your Home Assistant (i.e., Alexa or Siri) knows precisely what you are asking for, no matter how different your voice may be compared to your roommate.
Types of machine learning
There are multiple types of machine learning including:
Supervised learning: Takes existing data and responses and predicts responses to new data.
Unsupervised learning: Takes unorganised data and finds patterns without human input.
Reinforcement learning: Learns in an interactive environment through trial and error.
All three types aim to make the computer generate machine learning algorithms so that the system can essentially think for itself based on “experience” and data input.
Deep learning vs. machine learning
Deep learning is a type of artificial intelligence, sharing many of the same properties as machine learning. So, what’s the difference? Well, machine learning takes data and bases responses on the patterns it notices. However, deep learning takes this a step further and applies algorithms in layers to create what can be thought of as an artificial brain.
Deep learning creates a hierarchy of functions, working in a way that is less linear than machine learning alone. Using deep learning taps into multiple steps of data processing. For example, a machine learning algorithm will be able to notice anomalies, but a deep learning system will have better understanding of precisely why these anomalies have occurred.
How to use machine learning in accountancy
Machine learning has endless applications that can help simplify and improve business processes, and this is especially true for accounting. Here are just some of the ways machine learning can transform your financial processes:
Time is money, and errors cost an awful lot of time. Whether it’s an invoice error due to incomplete information or a transaction that failed to complete, machine learning can recognise what has gone wrong and not only flag it for correction, but memorise the error so it can prevent future occurrences.
Machine learning can allocate the correct invoices to the correct account. For example, if you often find your suspense account is quickly filled up due to lack of communication, machine learning can be especially useful.
Financial planning and analysis requires careful consideration of a business’s performance to predict its future. Machine learning can help make better predictions by analyzing and grouping data more effectively. By generating types of machine learning algorithms, unclear or unlabelled data can be sorted, to help with clearer planning that is free from any erroneous data insights.
Improved workflow through automation
Financial teams have one of the most important roles in any business, but that also means your accountancy team has to deal with a significant backlog of work, especially towards the year’s end. Machine learning allows for automation of admin like invoicing, freeing up your team for more important tasks.
Enabling the human touch
Counterintuitively, machine learning can give your team a better opportunity to add a human touch to your accountancy function. Free from being bogged down by data and analysis, automation and machine learning can lessen the workload and give your team more time to properly convey findings to clients in a meaningful, human way, not just in a hurriedly put together spreadsheet.
We can help
GoCardless helps you automate payment collection, cutting down on the amount of admin your team needs to deal with when chasing invoices. Find out how GoCardless can help you with ad hoc payments or recurring payments.