How Automation Will Transform Finance Functions
Over the past ten to fifteen years, automation technology has become increasingly important to the way finance functions operate. But the way automation is currently used is relatively basic - it mostly involves Robot Process automation, which fulfills simple tasks.
There is still considerable potential for expanded use with new technologies - according to research by McKinsey, 40% of the activities currently performed by finance functions can be fully automated, and another 17% can be mostly automated. Hyperautomation, based on AI technology that can execute far more complex duties, looks set to have a transformative effect on finance functions, offering.
Hyperautomation will be able to take on complex tasks, offering new levels of insight with automated analytics, holistic support on finance operations and automated communication with external stakeholders, while freeing up employees to spend more time on value-adding activities that require higher cognitive function.
Robot Process Automation
RPA is already used by many companies to perform basic, redundant tasks in finance operations, primarily supporting book-keeping, AP/AR, tax, payroll and expense management processes.
Not only does this save on people hours and allow employees to focus on complex tasks that yield more value, RPA is also able to complete these basic tasks far more quickly and efficiently than a human, without making errors.
The future of RPA in finance has two paths. First, training finance employees on RPA technology. Doing so means that rather than relying on increasingly overworked IT departments, the finance function will have the ability to directly work with RPA technology, avoiding bottlenecks and increasing efficiency of use.
Second, as the technology continues to improve, it will become increasingly integrated with other tools, based on AI and Machine Learning, creating hyperautomation.
Automated analytics
Already, some leading businesses are integrating automated analytics into reporting processes, combining RPA with Natural Language Processing technology to build monthly spending reports, collating KPI data and flagging any statistically meaningful changes.
This allows management at the FM and FC levels to spend more time on tasks that add value, such as refining processes or doing deeper analysis on issues that the data throws up, where they would have previously had significant amounts of their time taken up by producing these reports.
This is just the beginning of automated analytics, which will take on more and more reporting responsibility in the future. Eventually, it is likely to entirely replace month-end and year-end FP&A processes with real-time, continually updated reporting, offering the dynamic insights required by an increasingly complex and fast-paced business world. By eliminating human error and bias, it will also improve the accuracy of reporting.
Finance operations
As hyperautomation technology advances, it will reach end-to-end integration in finance operations, moving beyond the basic, repeatable actions of RPA. For example, an AI-supported collections process would involve:
AI accurately predicting and informing the Credit Controller which clients are likely to be late in making a payment, before outreach has been made.
A tool featuring Recommendation Analytics and Machine Learning recommending a set of incentives to encourage on time payment, and which customers are most likely to accept them.
Natural Language Processing making the entire collections process more efficiently by independently initiating outreach, intercepting client requests for payment extensions/late fee waivers, and making contextual, Machine Learning-informed decisions.
Automated responses
This area features some of the most widely used hyperautomation tools, in the form of chatbots and digital assistants, which are now commonly found on corporate websites across the internet. These use Natural Language Processing to take in user inputs and generate responses.
However, as anyone who has engaged with a chatbot likely knows, there is room for improvement. As it stands, most chatbots can only deal with relatively simple requests. But this is changing, following the steady improvement that has seen chatbots evolve out of email bots that send pre-programmed marketing messages.
Soon enough, automated response technology will be able to handle a majority of day-to-day requests made to finance departments, without requiring human input. This includes, for example, status inquiries about approvals or payments, requests for tax forms. Doing so will eliminate a significant expenditure of people hours on what are time-consuming and repetitive tasks.
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