Chief financial officers have always played a vitally important role in the long-term success of companies. However, fast-changing business environments, the rise of global competition and volatile economic conditions have complicated their role, forcing them to spend more time on day-to-day activities and less time on longer-term strategic planning. In a recent EY survey, more than 58% of chief financial officers (CFOs) felt that they do not have enough time to properly develop and define strategic decisions.
When asked about the new skills that CFOs require to realise the priorities of their finance function over the next five years, one of the most popular responses was the ability to develop their teams, enabling them to take on more of the finance chiefs’ day-to-day responsibilities. The CFOs believe that their team needs to have the ability to do the essential business of transactional management and stewardship which would free up time to focus on strategic priorities.
Among these day-to-day responsibilities; managing FX risks, ensuring the availability of credit, and driving working capital efficiency are the most important. Consequently, the CFO needs to be certain that these tasks will be handled effectively and efficiently. Technology can help.
Artificial intelligence can help make the transition work
Machine learning, leveraging rapid advances in processing power and accessing vast data sets, can help corporate finance executives to model forecasts and run predictive analyses for faster, more informed decision making. With machine learning and robotic process automation (RPA) capabilities, they can quickly interpret and act on sophisticated financial analyses, while also reducing the time spent on low value, error creating data entry processes.
Optimising working capital management
The role of the CFO’s team is broadening. They are now expected to play a pivotal role in working capital management which generally comprises of three key areas elements: payables, receivables and inventory. AI-powered solutions can help in making each of these processes more efficient and profitable.
RPA along with machine learning can help with the automation of complex treasury decisions that cannot be done solely using rule-based process automation. For example, AI-powered solutions can be used to streamline receivables processing using smart invoice matching. This involves studying payment patterns (via trend analysis) and constructing reconciliation rules such as LIFO, FIFO, normal match, best match etc. SAP has installed various machine learning and AI powered tools to drive efficiency in its collections process. In August 2017, Bank of America Merrill Lynch launched Intelligent Receivables – a solution that uses artificial intelligence (AI) to help companies post their receivables faster.
“Predictive analytics can enable treasurers to anticipate the likely outcomes of a wide range of events including FX moves, natural disasters, and geopolitical shifts.”
AI can help with payables as well – including enabling effective cash forecasting and the rapid detection of payment fraud anomalies. In addition, predictive analytics can enable treasurers to anticipate the likely outcomes of a wide range of events including FX moves, natural disasters, and geopolitical shifts. The solutions can provide detailed, timely insights which can be used to guide decision-making and prepare for possible events. The Hong Kong–based hedge fund Aidyia has gone a step further and is using AI to make all their trades, without any human intervention or support. It is easy to see a near-term future where Apple’s Siri answers questions on the latest financial results and forecasts, or where largely autonomous digital assistants proactively interrogate ERP systems to provide information before even being requested to do so.
Making supply chains more efficient
AI solutions can help streamline the procurement process with supply chain automation and more effective supplier management by providing prompt and efficient ways of answering invoicing queries from its suppliers. These solutions can generate real time-visibility of the spend data, which can be automatically classified and checked for compliance and exceptions. The real-time spend data helps in strategic sourcing and ensures that procurement gets the best deal it can from its suppliers.
California- based LevaData recently debuted Leva, the world’s first AI advisor for strategic sourcing and procurement while Salesforce ecosystem partner Apttus announced plans AI–powered contract management solution to help US public sector organizations modernize their procurement processes.
AI-powered solutions can also analyse finance data, procurement requests, tender approvals along with employee and vendor details to identify potentially corrupt or negligent practices. The Singapore government is already carrying out trials of using artificial intelligence to identify and prevent cases of procurement fraud.
The digitally augmented CFO
CFOs have always used tools and technologies to turn raw data into useful information and to turn useful information into actionable intelligence. As the volume and velocity of data increases CFOs must turn to the latest tools and technologies to help achieve their business goals.
Building on the traditional automation tools, AI solutions can be used to assist with more complex decisions, provide advice and recommendations to help the finance team. As AI is powered by its ability to gather, ingest and make sense of vast amounts of data, it learns as the data changes. The CFO can rest assured that the new tools and technologies will operate effectively and efficiency, freeing up time to consider the company’s strategic direction.