Previously published in The Digital CFO Magazine
Finance often gets faulted for lagging significantly behind in terms of technological innovation. However, it’s not hard to understand the reasons for their hesitancy. For a department that must sign their name and their reputation to the truth and fairness of financial statements, it’s understandable that placing these processes in the hands of technologies they don’t feel they control would make them nervous.
This is not to say Finance has spurned all cognitive technologies. Robotic Process Automation (RPA), which involves the use of software bots to automate repetitive, mundane, and routine business processes, has been increasingly adopted by finance teams over the last 5-7 years. From automating data entry to filling in custom information in a CRM, to enabling insurance policy comparison, RPA software has been a fairly risk-free way of allowing enterprise organizations to automate tasks that require performing the same action over and over again while not posing a significant risk to the business.
There are clear differences between RPA, Machine Learning (ML), and Artificial Intelligence (AI). RPA involves bots that mimic human actions, but it lacks any built-in intelligence. Machine learning uses structured and semi-structured historical data to “learn” and make predictions without being explicitly programmed, but it only works within predefined knowledge areas. Finally, AI relates to algorithms that imitate human behavior making it a decision-oriented technology. Some organizations have used a combination of RPA and AI, called Smart Process Automation (SPA) which extends the scope of RPA.
While the adoption of Machine Learning and AI is generally slower within the CFO office, there are a number of finance teams using it successfully and it’s poised to be a significant differentiator in the coming years.
Stepping up during a pandemic
At a recent conference, the finance team at a large provider of services to the travel industry, spoke about the role of AI during the Covid-19 pandemic. In the past, finance was focused almost exclusively on a historical view of the business and generating the required statutory, management, and regulatory reporting. They were seen as the bean-counters, always looking back at the past instead of predicting the future.
However, during the pandemic, stakeholders from across the business came to finance asking for a view of potential scenarios. The finance team used an AI solution to generate potential outcomes based on hundreds of potential possibilities. Could they have used a giant Excel sheet? Of course. But the use of AI provided the transparency, automation, and processing power needed to put finance in the center of business success.
Guiding Business Decisions
Advisory firm, KPMG recently presented at AptConnect 2021 about how their solutions, KPMG Signals Repository and KPMG Intelligent Forecasting are providing forecasting and analysis that can help guide business decisions. During their session, they referenced a 2019 video that featured a CFO in the near future who was able to stay one step ahead of the competition because she had the technology and the tools to quickly run scenarios and forecast the impacts of various decisions.
David Fourie, KPMG Partner, and session presenter argues the future depicted in that video is now a reality. “Today we’re seeing technology solutions that can support the future of finance – solutions that are best of breed, integrated, cloud-native, and AI-enabled.” CFOs who have embraced these technologies are already outperforming the competition and that digital divide between finance organizations that can make use of these cognitive technologies and those that can’t is going to widen further in the coming years.
During their session, David and Aptitude Senior Solution Consultant, Matt Kelley, walked through a demo illustrating how a CFO at a General Insurer looking to expand into a new state could use historical data at the policy and coverage level, held in the Aptitude Accounting Hub (AAH), to generate forecast models that can be augmented with data from KPMG Signals Repository. Signals, which uses structured and unstructured data to create complex expressions on anything from crime rates to home prices, can then apply those to subledger data to generate the most advantageous locations for expansion – in this case, three counties within a specific state. Once the new locations are up and running, AAH can be configured to account for the new business and KPMG Intelligent Forecasting, using the AI technologies in the KPMG Ignite platform, can generate actionable forecasts for the newly selected locations over a certain period.
Watch the 20-minute session
The continued application of AI and Machine Learning technologies in the finance department will only increase. A 2021 McKinsey survey on AI usage, showed a 30% jump in the percentage of respondents citing cost decreases stemming from AI adoption within the risk and the strategy and corporate finance departments between 2019 and 2020.4 While managing the risks of AI in the finance department will always be a consideration, CFOs must get on board or their organizations could be left behind.