CFO Stack technologies pt 1
In today’s fluid market landscape, variations in consumer demand, supplier inventory, and infrastructure capacity constantly recalibrate operational decision making. One vector that serves as an anchor for many teams’ strategic planning are financial forecasts. A simple forecast can serve as a lighthouse amidst the sea of uncertain market forces and help teams set achievable near-term goals, procure inventory efficiently, and portend the financial implications of their decisions.
Today’s current Financial Planning & Analysis (FP&A) teams are predominantly rooted in static spreadsheets and insular cubes that provide limited visibility to outsiders and are challenging to refresh. Forecasts may be shared with adjacent business partners but very seldom do teams maintain the channels by which to input their prognostications directly into the FP&A matrix. In order to obtain guidance from other stakeholders in the firm, finance personnel must often reach out manually to those practitioners and solicit data/feedback a la prolix, opaque communiques that often leave much lost in translation. On top of this, forecasts remain up-to-date with only the latest period’s published financials oftentimes creating a dead zone of up to a month where financial statements remain stale and less reliable. With APIs linked to accounting software such as Xero and Quickbooks, nouveau Finance-as-a-Service (FaaS) solutions can update financial statements in real-time with flowthroughs directly from the General Ledger. But this by essence is still a reactive mechanism, how can financial Strategy actually be influenced by such timeliness? This is where innovations in AI, ML, and NLP come in. As petabytes of varied consumer data accrete in the cloud, ML engines can be leveraged to mine this information for insights and relationships. These nuggets can then be used to produce robust forecasts substantiated by multifaceted information. As workforces continue to evolve to more of a highly skilled, service-oriented nature much of the labor in analyzing data will need to be automated. As AI picks up the slack in relation to human capital, finance professionals can spend more time on winnowing trends in the business and providing proactive recommendations on strategy, such as capital allocation, cash flow optimization, and expansion analysis. This is where I see the morphology of a modern-day CFO trending, down a path towards more preemptive oversight of the business versus historical analysis and reconciliation.
Startups have begun taking heed of the exigency towards financial transparency and furnishing solutions that can democratize economic information, capture input from non-finance teammates, update financial statements in real time, and offer prescriptions for the future. Many firms are using AI, ML and NLP to connect unstructured data from the GL/ERP/CRM with traditional information found in spreadsheets to create a richer understanding of the business. As relationships between multifarious IT systems are formed, a centralized finance solution can help different business stakeholders better understand the financial impact of their decisions.
I posit that the future of corporate finance tech will gravitate towards more of a SaaS-based model where companies will be able to use their transactional, internal, and supplier data to power AI & ML-informed forecasts and analysis. The legacy approach to financial analysis has largely been anecdotal and assumption-based but with technology evolving it is time to take stock of the reagents making augmented intelligence a reality.
For inquiries please contact rgupta@mba2022.hbs.edu
