Why automating finance is just an integration game | Computer Weekly (2024)

Make automation count

In an ideal world, the tech is new and shiny, compatible and connects without any hint of interoperability or latency issues, but in reality, legacy technologies tend to throw a few spanners into the works. Understanding how to manage this cost-effectively is key to progress and one of the sticking points for current transformations. While organisations may understand the vision and the value of automation, it can also seem an expensive problem to solve and scale.

According to analyst Forrester, successful automation projects share certain characteristics – outcome clarity, stable data, alignment of digital skills and acceptable governance and control. The value to the business and the potential to transform and disrupt the current state also drive adoption.

How does this marry with the real world? According to Colm Carey, chief analytics officer at AA Ireland, a Tibco customer, there’s a huge push for new data and models within the business, to identify and then select profitable customer types. It means the firm’s automation processes have to get a lot more sophisticated.

“Our goal is to optimise pricing, understand the types of customers we’re bringing, and the types we’re trying to attract,” says Carey. “We would like to tie that across the business. Marketing will run a campaign, trying to attract a lot of customers, but maybe they’re not the right type. If the call centre is going to be very busy that week, what’s the point of driving a lot of demand there?”

It’s here that finance data becomes key. The business needs to evaluate huge volumes of data to understand the market, its customer base, and also its own financial status. To have a workable, predictive model requires financial data at its core.

“Data comes in and goes out to models seamlessly without disruption, basically providing real-time predictability,” says Carey. “Rather than just predicting, it’s ‘If I increase or decrease discounts, what’s the uplift in volume and profitability?’”

Build trust with the basics

Alessandro Chimera, director of digitalisation strategy at Tibco adds that when it comes to the finance function, it’s also about trust, the inference being that there is a cultural issue to overcome, not just with customers but internally too. AI-driven finance functions, in theory at least, should be more accurate, but of course automation also changes processes and roles. Expectations change, as do job descriptions, but for many organisations this is just a progression. It’s been coming for a while now – it’s always been about the “how”, not the “why”.

Craig Le Clair, vice-president principal analyst at Forrester, says basic automation, such as workflow for closing the books, or simple reconciliation, and RPA for repeatable tasks are widespread today. On the flip side, he says: “More advanced automation that uses machine learning for prediction or text analytics to extract data is hampered by the extensive variation in process steps across firms, which prevents vendors from development of packaged AI solutions. Outcome clarity, stable data, lack of digital skills and brittle legacy systems contribute as well.”

For finance, this is a huge challenge. For something so central to organisations’ abilities to find efficiencies and create credible forecasts, this needs to be addressed. So, where do organisations go from here? What are the priorities?

“Look to start with simple task automation or RPA,” says Forrester’s Le Clair. “This is a quick win area. Some 20% of RPA deployments are in the finance and accounting area due to extensive low-value manual operations that drive from older on-premise systems. Integrators will help identify a promising task, run a POC [proof of concept] on the server, and if successful hope to get the implementation to work.”

This resonates with Prophix’s Ajmera, who says RPA in finance and accounting is basically the same as for any other industry – the automation of tedious, high-volume and repetitive tasks. What automation will do here is ease pressure on finance teams that are seeing increased demand for accurate, real-time data to feed overall business planning.

“Most finance teams are already overburdened with day-to-day tasks,” says Ajmera. “Using corporate performance management (CPM) software with RPA and AI technologies built-in enables finance teams to automate repetitive processes and provide valuable insights faster. Examples of this include streamlining reporting by having the technology automatically populate reports and provide row- and page-level narration, with the software assembling the report and distributing it to management or the board with a click of a mouse.”

What is clear is the increasing demand for decision intelligence with financial analytics at its heart. RPA suppliers are increasingly repositioning themselves as automated intelligence companies, using RPA tools to drive key functions, such as finance. Gartner believes a third of large organisations will be using decision intelligence for structured decision-making to improve competitive advantage in the next two years.

Recent research by enterprise application integration firm Jitterbit backs this up. Focusing on mid-sized companies (referred to as Mittelstand) in the DACH region (comprising Germany, Austria and Switzerland), Jitterbit found that 73% of these businesses want to be hyperautomated within three years because “the health of their company depends on it”. The barriers to achieving this are typical – too many manual data process, isolated data silos and a lack of departmental integration.

What is becoming clear is that financial analytics can be the core and the catalyst of intelligent automation transformations. If organisations can get this right first and feed from it, the process of driving AI automation across departments will become much easier. At a time when budgets are being squeezed and any transformation scrutinised, focusing on finance seems like an obvious prioritisation strategy.

As a recent Accenture report suggests: “By using data better, finance can be more proactive, more predictive and more strategic. A large amount of value can be released by starting with the basics: making the data itself more descriptive to minimise cleaning, interpreting and duplication; and making it easier for multiple departments to use and understand to diagnose where improvements can be made.” The future of organisations depends on it.