Machine learning can have a big impact on the professional services industry. It can be used to perform and accelerate repetitive tasks and to accurately predict and act on current and future workload and employee needs. Where companies struggle to deploy the right resources to the right projects, machine learning can improve speed, quality and efficiency by continuously learning from previous decisions. This blog will shed a light on how machine learning can be used to improve performance and bottom line results in the professional services industry.
Machine learning in Professional Services: the time is now
The professional services industry has always heavily relied on knowledge and therefore new technologies have not impacted the industry as much as, for example, manufacturing companies. However this changes with machine learning. Repetitive professional services tasks are being automated as machines can process data faster, which results in better performance. As a result, the job mix within professional services companies will change. Repetitive, entry-level tasks will continue to dwindle as they are being taken over by machines. Service businesses worry about a future where the sustainability of their business model is under pressure. However, demand will persist in areas of human work, namely the jobs more subject to human decisions, specific expertise and insight.
Auditing for professional services
We are talking about machine learning when software programs can literally ‘learn’ from the past to make their decisions. These programs can recognize patterns, see trends in existing data and use this to perform actions and make predictions. A prime example in professional services is Auditing. Firms can use machine learning algorithms to process and evaluate all transactions in a given time period to determine irregular or malicious activities. Where this might sound like it removes a lot of work, these algorithms are far from waterproof as of yet. Current results show a lot of exceptions which in turn provides a lot of human work.
Interpret and process unstructured data
Another area in which machine learning is applicable is the combination between natural language processing and cognitive computing. With natural language processing it is possible to interpret and process unstructured data like documents and emails. Cognitive computing can use this newly acquired data to analyze it and act on it. This can be used to analyze customer contracts and perform tax returns. Where in the beginning the actions and decisions will need to be evaluated and corrected by humans, this is gradually decreased due to the machine learning aspect.
A more practical example of machine learning in professional services is the automation of the sending of customer reminders and invoices and interpreting incoming invoices to detect fraudulent ones. These are relevant activities for any professional service company. To elaborate further on the topic of invoice matching, SAP offers their Cash Application which automates the invoice matching processes. Through machine learning the system is able to interpret past matches and clearings to continuously increase the accuracy of invoice matching. Already in 2017, chemical company BASF has achieved a success rate of 94% when it comes to ‘auto-matching’ incoming payments with invoices. Another Machine Learning capability offered by SAP in the same area is a feature in the GR/IR (Goods receipt/Invoice receipt) application. Based on past data the system can predict the next status of the item. This makes it easier to reconcile the GR/IR accounts and to monitor these reconciliations.
Improve performance with machine learning
It might seem that the term machine learning is thrown around as a buzzword, but it is far more than just that. Machine learning is reality and this will only increase in the future. Where machine learning pioneers at the moment consist more of big companies with large budgets, new technologies will be more accessible as time goes by. Are you curious how SAP and SOA People can help improve the operational and financial performance of your professional services organization? Please contact Sylvain Vrolijk (the author of this article) or Jorg Setz via phone number +31 (0)30-6096800 to discuss this topic in further detail.