Key takeaways

  • CFOs need to reach across enterprises to act as a catalyst for innovation.
  • RPA, AI and blockchain will dramatically reduce inefficiencies, giving finance professionals the time needed to create enterprise value through data analysis.
  • A clear pathway to technological implementation will enable the finance function to own business data, ensure operational relevance and accuracy and provide strategic insights for the future.

 

Arguably, the chief financial officer has the most important job in the office these days. In a data-driven era, it is the CFO who must own and analyse company data and extrapolate relevant operational and strategic insights.

A newly released PwC report Confidence in the future: How tomorrow’s technologies can help the finance function of today underscores the importance of the chief financial officer, not just to the finance, but to the lifeblood of the business – the data.

Currently, CFOs are swamped in transactional tasks, many of them mundane and rooted in collecting data, rather than analysing it. Technology, however, is providing viable options to free up the time of finance professionals – by up to 46% – allowing them to harness deeper, real-time insights and create organisational value.

Time-saving tech:
five use-cases

Three emerging technologies, robotic process automation (RPA), artificial intelligence (AI) and blockchain are all avenues business can use structured and unstructured data to unlock enterprise value.
Tangible cost efficiencies can be achieved by automating transactional tasks, which plays a major role in giving leaders an advantage. RPA, when applied to finance processes, is relatively quick to implement and the impact swiftly felt in cost and cycle times.

 

Here are five approaches to consider when assessing the interactions between emergent technology and finance:

  1. Efficiency – Instead of pulling data from different systems, spreadsheet-ifying that data and feeding the results into yet another system, process automation could handle the entire process. For example, automating a process so that a requisition turns into a purchase order, is sent to the vendor and is then authorised for payment.
  2. Quality – Anomaly detection and machine learning can be used to analyse and scan transactions, which in the long-term could provide a continuous audit of real-time assurance.
  3. Optimisation – Balancing performance and risk among P&Ls could be done via machine learning and agent-based modelling techniques to enable a business determination on the optimal balance sheet, informing operational decisions.
  4. Compliance and risk – Natural language processing and RPA, combined with AI, can leverage unstructured data, such as contracts, to offer new forms of assurance and insights.
  5. Assurance – As new technologies are implemented there will need to be a greater input from finance teams in any process changes to ensure compliance and risk is addressed.

A pathway
to implementation

Some of these technological uses can be implemented relatively quickly in the short term, while others may take longer as processes and technology matures. Regardless, CFOs need to consider how to go about spearheading the technology’s introduction.

The report is clear that there is no single pathway that can be applied to all businesses, however, it does list potential catalyst points that will go a way towards facilitating technological adoption. These are:

  • Start small – Begin with discrete projects or pilots that don’t require capital expenditure and will deliver returns as proof of viability. This will also allow time to address capability issues along the way.
  • Draft your vision – Ignoring budget constraints (a tough ask for many a CFO), outline the capabilities desires for the finance function for 3-5 years, considering near, mid and longer term objectives. This will allow the team to prioritise activities and projects that can be achieved in the short term, and pave the way for greater innovation to come.
  • Develop a data governance strategy – Assess data flows and governance requirements for each of the defined projects and the finance function as a whole. Data stores are often ad hoc and their administration uncoordinated. Ask what the key data assets are, find out who has access to them today, and consider who should access to them in the future.
  • Get involved in enterprise analytics initiatives – Own the analytics to ensure that operational metrics always contain a financial component in order to be strategically useful (and accurate).
  • Hire talent and promote innovation – Think these are IT roles? Think again. PwC research suggests that data and tech skills will increasingly become integral to finance. It will be important to bring data science, visualisation and programming skills into finance in order to uncover opportunities and achieve value.

Technology
by the numbers

By embracing these technologies as they are today businesses will be well placed to capitalise on the gains that will come from advanced artificial intelligence applications and the blockchain maturation tomorrow.

By owning all the data in the enterprise and using emerging technological processes to analyse it, CFOs will be able to promote a shift toward technology’s innovative edge across finance and the greater organisation.

The subsequent culture of cross-functional partnerships are what will enable future value creation, not just for finance but for the whole business.


Download Confidence in the future: How tomorrow’s technologies can help the finance function of today for further detail on making use of emergent technology in the finance function.

 

Contributor

John Shipman

John Shipman leads the fintech space in Asia for PwC Australia.

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