SynergERP webinar unpacks the different standards CFOs have for technology and humans.
In a recent webinar Dennis Gannon, VP of research at Gartner, suggested that CFOs have “algorithm aversion … holding technology to a different standard than they hold humans to.” He explained that a CFO will accept a 10 percent variance in a forecast based on human judgement, however, they will only accept a five percent variance in a forecast generated from autonomous digital agents. This implies that to truly start to work with these self-learning and self-correcting technologies in Finance, CFOs will need to have a mindset change. Going forward, they need to give the outputs from these digital agents as much credit as they give to humans. So, where are we now and how do we progress towards working with self-learning digital agents?
One way to think about robotic process automation (RPA), machine learning (ML), and artificial intelligence (AI) is as an automation continuum. For simple, repetitive and highly manual processes with easily defined rules, RPA can handle these tasks. For more complex tasks where there’s more variability in the data, machine learning is best. When more intelligence is required —where the data is more nuanced —AI is best suited.
An example of AI in action is outlier detection. AI can learn transaction patterns and flag new transactions that don’t match these patterns. It can provide information as to why something is flagged, so there’s no guesswork. AI will also look at what happens to the transaction once the human has agreed or disagreed with the classification, to learn whether the transactions flagged were indeed outliers. This learning allows for better prediction of future outliers.
The objective of automating the underlying financial processes with tools such as RPA is to make more time available for analysing this type of outlier information along with the overall business performance.
Choose financial software with embedded AI
You can take advantage of the latest technology by choosing financial software with embedded intelligence. An example of this is Sage Intacct’s cloud financial management solution which includes Intelligent GL™. This functionality enables you to review thousands of GL transactions using AI-powered outlier detection to pick up anomalies.
In a modern cloud ERP such as Sage Intacct, data can be captured in real-time through open integration with other applications, financial institutions and suppliers. To get the most out of any system, ensure you set up the correct data structure at the start of a project – see your data as an asset. Also, consider any current or future integrations that may be required to access other data sources.
By adopting the best-suited applications and automation tools a business can ensure they always have accurate, current data that is ready for reporting and analysis. A financial review then becomes a continuous proactive process.
So, you’re on top of current reporting, let’s look at forecasting
Cloud computing has enabled multiple technologies to come together and work cohesively.
It also offers organisations the ability to tap into short bursts of computing power to handle sophisticated AI-driven forecasting models.
There are a number of corporate performance management (CPM) tools available in the market that finance teams are using for forecasting and budgeting. SynergERP client Kreason Naidoo, financial reporting manager at Steyn City, described his experience with Prophix CPM as follows,
“When we first started the process, I didn’t know how powerful the tool was … But then, as we were building, we realised that my budgeting process is simplified by weeks. My forecasting process is simplified by months. We forecast here up to 2026, and sometimes 2032, and that’s something that can be done in a week.”
Prophix's product offering now includes the Virtual Financial Analyst, an artificial intelligence-powered assistant. It helps the user to identify anomalies and automate FP&A tasks.
This application includes a Task Assistant which leverages natural language AI to enable a more natural method of communication between the user and the application. There are two ways to make a request to the assistant, either via a typed request in the interface or, by simply asking it to complete an action by talking to it. An example may be, asking the Task Assistant to populate a forecast with prior year actuals as a starting point for a forecast.
Automation: What’s next
Finance technologies are quickly evolving from simply offering automation, to offering a selection of self-learning software agents, described in the market as ‘autonomous finance’.
CFOs need to ensure that their beliefs and assumptions about technology are not limiting progress towards an AI-supported organisation.
In the same way that digital assistants at home play your favourite song or order your weekly grocery list, there is similar potential for artificial intelligence, natural language queries and machine learning to transform the Office of Finance.
SynergERP as a provider of business solutions and automation tools is well-positioned to assist you on the journey to employing more digital assistants.