Dzingira Matenga | Managing Director | CFO & Enterprise Value | Accenture in Africa | mail me |
As CFOs take on a more strategic role, other functions are increasingly looking to finance for near real-time and more accurate forecast reporting to make more informed business decisions.
Although historical reporting remains relevant, organisations are looking to the finance role for instant guidance amid changing market dynamics.
New technology and the more imaginative use of data can enable finance to fulfil these expectations.
Harmonised data platforms, advanced scenario modelling, and the adoption of predictive analytics models are among the essential tools that CFOs can take advantage of to answer those ‘what if’ questions with confidence. This is the predictive view to finance.
Out with the old
By transferring data from old architectures into more single data lakes or cubes, combining internal performance data with a broader range of external sources, and exploiting the skills of data scientists, finance can produce far richer insights.
Predictive analytics provides the key to planning investment, evaluating customer models and value proposition shifts, and assessing future scenarios. But old legacy systems pose a hindrance to these potential benefits.
Yes, legacy systems that have long served the finance function are being phased out, but slowly. According to our research, although 60% of traditional finance tasks are automated and cost efficiencies have been gained through enhanced reporting and operating model maturities, only 43% of CFOs have used advanced financial modelling to identify future risks, and only 37% used it for shareholder value creation.
This backdrop becomes even more complicated as CFOs support new responsibilities and areas of focus. 68% already say that finance takes ultimate responsibility for sustainability (ESG) within the organisation. In the last two years, some of the most influential CFO’s have been instrumental in driving initiatives outside of finance including new business models (41%) and revising corporate strategy (40%).
Overall, our analysis shows that CFOs who have developed additional specialised abilities in strategic planning, analysis and advisory can move top-line growth and bottom-line profitability. Statistical modelling revealed that with such expertise in place, S&P 500 companies could increase their EBITDA compound annual growth rate (CAGR) up to 6.9% over the next three years.
We also found that they could improve their CAGR by up to 3.0% for a $20 billion company that translates into tangible value.
Teams get stuck in the past
CFOs already view themselves as the ones to take the lead in planning and realising the future. Some 75% believe finance is best placed – of all the organisation’s functions – to help the business understand the economic model underpinning new technology investments.
Moreover, 82% believe the ability of finance to forecast the long-term value of specific technologies is much greater than that of other functions, including the IT function itself.
The trick is to get your entire finance team into the new way of doing things. CFOs need to free up capacity in the finance function to allow people to spend time doing more future thinking. Many finance teams are bogged down in very convoluted processes, either manual or improved, but highly complex. If these processes are automated, you free up time to focus on predictive analysis.
Next is to re-train your team to be more analytical. They have spent a long-time processing invoices, doing month-end journals, and balancing accounts that they need guidance on how to shift the way they use data. Teams can churn through the data through machine learning or artificial intelligence (AI), identify trends, and pull new insights to make more informed decisions.
Here are a few ways finance teams are putting predictive analytics to use:
- Predicting revenue – Marketing, sales, operation, and even customer behaviour data makes it possible for finance teams to forecast revenue more accurately and anticipate future demand for products.
- Enhance capital allocation – Organisations can apply predictive analytics to project data to better understand portfolio velocity, the likelihood of projects being delivered to time and budget, and realistic benefits realisation across capital projects.
- Improving supply chains – Enterprise development including identifying and investing in specific suppliers in return for the security of supply and lower-cost products, analysis of key supplier financials to identify price sensitivity and support procurement negotiations.
- Analysing loss drivers – Zero-based budgeting and improved cost containment including the application of economic profit models for standalone business units.
- Detecting fraud – In many companies, finance teams use predictive analytics to identify potentially fraudulent activity and patterns.
Adopt with caution
Finance has always had the benefit of access to a rich data set from which to extract insight. Adopting a predictive view could be a simple intervention but it needs to be done with caution.
Targeting customer data can lead to privacy issues, the potential misuse of data and even discrimination lawsuits. CFOs and their teams must therefore exercise ethical oversight as they ramp up their predictive analytics capabilities.
