A Practical Guide to Emerging Technology

by FEI Daily Staff

How tomorrow’s technologies can help the finance function of today.


Finance executives who want to get started with emerging technologies can simply focus on one word: Data. CFOs are swamped in data, coming from ERP, payment processing, business intelligence, and other structured databases. They also have a treasure trove of unstructured data, from legal contracts and emails to videos and interview recordings. The ability to work with that data to improve transactional cost efficiency, address risk, and harness deeper, more real-time insights to identify new sources of company value is a competitive necessity for finance functions.

Three promising technologies are robotic process automation (RPA), which automates processes across multiple systems by using software tools accessible to non-technical users; artificial intelligence (AI), which includes a set of technologies like machine learning that sense and analyze data, learn from it, and perform actions based on it; and blockchain, which is a decentralized ledger of all transactions across a secure peer-to-peer network that can replace processes where transactions need to be verified by a third party.

These technologies may seem like daunting and unproven exercises best left for future leaders. But the reality is that they are here today and CFOs can use them in discrete ways to create immediate benefits. In so doing, the projects pave the way for future success. For example:
  • Use RPA to make accounting more efficient—and open the door for more machine learning: One manufacturer is using RPA and machine learning to automate an accounting process from requisition to purchase order to payment. It will take only a few months to implement, and there should be a big drop in invoice holds and manual journal entries. The discovery that automation can deliver roughly 30 percent to 40 percent process savings is also opening the door to more collaboration between the CFO and CIO and creating more openness to applying advanced forms of machine learning to finance data.
  • Enhance audit quality with machine learning: Algorithms can smartly examine more and more transactions. Machine learning can help analyze new kinds of transactions and scan more transactions. Scanning more and more transactions will open future possibilities to encompass all transactions for discrete portions of the audit, until eventually the audit will scan complete populations of transactions—rather than sampling.
  • Optimize strategy with machine learning—and generate more strategic insights: By using machine learning and agent-based modeling techniques, a bank can model an optimal balance sheet to inform operational decisions while staying within the company’s risk appetite. Future insights generated by AI can be derived from data beyond the historical scope of traditional financial reports.
  • Examine contracts for compliance and risk—and learn to leverage unstructured data: When lease accounting standards changed in 2016, finance departments needed to re-review file cabinets full of lease contracts to bring them onto their central balance sheet. Luckily, with natural language processing, RPA and machine learning, departments could automate lease accounting contract review and simplify compliance. Going forward, increasing amounts of unstructured data can be “ingested” by AI engines, offering new forms of assurance and insights.
  • Assure the blockchain—and build trust in emerging technologies: Emerging technologies that solve business problems also should factor in contractual agreements, regulation and risk mitigation processes. For example, a blockchain public ledger potentially renders moot the audit processes that look back at historical transactions, so organizations will need to turn to real-time assurance before transactions are written to the ledger. The CFO and internal audit teams should be at the table early, so process changes include compliance and risk considerations.
Those are a few examples of projects that generate immediate benefits and that suggest pathways to build capabilities for the future. There are many other possibilities, which underscore the fact that there isn’t a single approach for implementing emerging technologies in finance. Still, certain catalysts will help to facilitate their adoption.

To be sure, while RPA and some basic AI can be implemented in the near-term, more advanced forms of AI and blockchain will take longer to roll out. But the organizations that begin to deploy emerging technologies now will be in the best position to exploit them in the long term.

Mike Baccala is Assurance US Innovation Leader at PwC.