FP&A – just for budget time?

By Simone da Silva Collins, Finance Business Partner at The Warranty Group

Simone da Silva Collins is an FP&A professional working in The Warranty Group, one of the world's premier global providers of warranty solutions and related benefits.

She provides business partnering and assists in development of MI to operations heads at TWG. Prior to joining TWG, she was the Finance Manager at Polycom (taken private by private equity firm Siris Capital Group) and provides business partner support to various departments in EMEA. She was previously the Group Finance Analyst supporting the Executive Team at Intec (now part of CSG), a provider of Business Support System (BSS) software and related services, primarily for the telecommunications industry. She also worked for Telewest (now part of Virgin Media) for over 7 years providing commercial and financial support to the Interconnect team.

Simone is originally from Macau in SE Asia. She gained her Masters at Manchester Business School. In addition to being ACCA qualified, Simon has also achieved FP&A accreditation.

teamAt a recent FSN webinar “Why are some originations more insightful than others1?”, I heard about an illustration of possibly the earliest example of what FP&A is about - forecasting, planning and providing actionable information. Most of us are familiar with the biblical story of Joseph’s interpretation of the Pharaoh’s dream. The Pharaoh dreamt of seven fat cows being swallowed by seven thin cows and seven ears plump corn swallowed by seven ears of thin and blighted corn. Joseph, the data scientist of Ancient Egypt, provided an insight and forecast to the Pharaoh: the seven fat cows and the seven ears of plump grains represented seven years of good harvest which was followed by seven years of famine. Joseph used the “data” (loosely applied in this case) based on Pharaoh’s dream to forecast over a 14-year period (seven years of good harvest plus seven years of famine). The actionable information (interpretation of the dreams) led the Pharaoh to take corrective action and put in store harvest from the good years to be used during the famine years.

However, when we talk about FP&A, the focus sometimes weights more on the planning than the analysis – weekly forecast, monthly forecast, quarterly re-forecast and annual budgeting. The provision of insight and be a strategic partner to the business can sometimes become a “by-product”. I believe that FP&A goes beyond just planning and analysis. FP&A professionals are the intelligent satnav of the business. As it was quoted by  AFP, “FP&A is evolving from an offshoot of the accounting organisation into a forward-looking, strategic function”.

However, before I embark as to why that FP&A involvement goes beyond planning, I’d like to answer a question that I often get asked when I tell people I work in FP&A – what does an FP&A do?

In its most basic form, FP&A analysts collect and consolidate data and prepare some form of management reports. In recent years the profession has become more of a business partner: they do not only churn reports or process transaction data. They help drive business forward with sound advice from a finance perspective. This distinguishes FP&A professionals from the traditional analysts. To expand this further, let’s first look at an academic definition of the word “partner”.

Oxford Dictionaries defines “partner”2 as

  • Either of a pair of people engaged together in the same activity
  • A person or group that takes part with another or others in doing something
  • Any of a number of individuals with interests and investments in a business or enterprise, profits and losses are shared

First Eureka! moment - shared the interest in a business. In general, everyone has a common interest with respect to the performance of the company in which they work. They want the company to perform well. From a financial perspective, this usually means profits, cash surplus, a share price that reflects the company’s intrinsic value (which also allows the shareholders to generate wealth when holding and/or trading in them). With this shared interest, technically each employee is a partner to the business: each contributes to the performance of the business. Some companies refrain from the term employee but refer to the term partner or associate to reflect this philosophy.

Armed with this basic understanding of business partner, we can look at what an FP&A business partner does.

FP&A gather data and apply their analytical expertise to provide insightful information to operational and organisational partners. They take a keen interest in the well being of the business from a finance perspective. They engage with stakeholder to understand their need for actionable information. They concentrate more about what is likely to happen as opposed to what has happened. FP&A use their expertise to provide sound advice to management. FP&A takes a large volume of data and use analytics to derive insightful correlations and trends as well as anomalies. This helps to generate actionable information that management can use to drive performance. It is about providing the right “ammunition” for the business to “defend itself” against the ever-changing environment. FP&A acts as the intelligent satnav. The organisation knows the path they need to take to succeed. FP&A walks this road together with the business and along the way take into account updated “road and traffic conditions” to provide advice and recommendations of corrective actions. FP&A insights tend to have two purposes: a holistic view that promotes collaboration between departments and helps management drive performance to achieve their strategic goal. They help the company to be agile.

FP&A Business Partnership Maturity Model

At their FP&A Circle meeting in London on 25th January 2018, the International FP&A Board presented the FP&A business partner maturity model.  The 3 stages of Basic, Developing and Leading echoes the role of FP&A business partner – a trusted advisor and key influencer who acts as the sounding board through the use good analytics in challenging the business.

maturity model

How does FP&A go about providing insight for the business? This model provides 4 areas of development. Apart from the essential technical knowledge of their area and gather necessary data, FP&A professionals make use of analytics as well as soft skills to gain business knowledge and provide forward-looking insights of the business. At the Leading State, FP&A move away from traditional budgeting models and go beyond budgeting. This enables FP&A to focus on developing analytics which helps management be more agile and proactive to the ever-changing environment.

A brief encounter with an FP&A

I’d also like to mention a few words on what makes a good FP&A. There are a string of qualities that FP&A business partner posses which promote them as a strategic partner.  Amrish Shah lists these qualities in his recent article “I hear you want to be an FP&A professional. Well, then you need….3. I’d like to add to this list

  • Good communicator – This is not just about the verbal signals and exchanges between the speaker and their audience but also all the non-verbal signals. It also means promoting knowledge share and not fall into the trap of becoming a rigid gatekeeper of knowledge.
  • Collaborator – it is important that different parts of the organisation work together toward a common goal. It sounds cliché but this is critical in developing cohesive working practices and promotes value-added analytics and smarter decision making. You can read more on this topic from an article by Nilly Essaides “10 Ways to Enhance FP&A and Business Collaboration”4

So, to where does this all lead us?

FP&A does not work in the finance silo of providing backward looking analysis of what has happened. They arm themselves and their operational and departmental leaders with the “why” of the performances and develop insights on trends. FP&A uses available technology and big data to their advantage. In the leading state of the FP&A Business Partnering Maturity model, FP&A is a credible partner that the business leaders can rely on when developing a sound strategy.



Any views or opinions expressed are solely those of the author and do not necessarily represent those of The Warranty Group.


1 Why are some organisations more insightful than others?

2 Oxford Dictionaries

3 I hear you want to be an FP&A professional. Well, then you need….  by Amrish Shah

4 10 Ways to Enhance FP&A and Business Collaboration by Nilly Essaides

How to Manage Forecasting Risk?

By John Stretch,  MD at Stretch Business Training


“The Bank of England’s chief economist has admitted his profession misjudged the impact of the Brexit vote. Blaming the failure of economic models to cope with “irrational behaviour” in the modern era, the economist said the profession needed to adapt to regain the trust of the public and politicians. The bank has come under intense criticism for predicting a dramatic slowdown in the UK’s fortunes in the event of a vote for Brexit, only for the economy to bounce back strongly and remain one of the best performing in the developed world. 

Official figures have shown the economy was outstripped only by the US among the large economies last year after growth in the third quarter was upgraded to 0.6%. Forecasts by the Treasury, the International Monetary Fund and the Paris-based OECD, all pointed to a recession after the vote, based on assumptions of steeply declining consumer spending and business investment.

He blamed the profession’s reliance on models that were built for an age when consumers and businesses, and especially banks, “behaved rationally”.  Answering critics of the bank’s gloomy November forecast for the economy, he admitted that the bank did not anticipate the resilience of consumer spending after Britain voted to leave the EU. “
Source: The Guardian, Thursday 5 January 2017. (See Note 1)

At the end of each financial year,  many companies are busy finalising their annual budget. But how do you budget in a world where the best economists cannot predict the future? Perhaps the only approach is to prepare a budget as best you can, live with the uncertainty and then adapt to it and respond very fast when the future eventually happens.

This may explain why organisations have converted their annual budget process to a dynamic system of rolling forecasts. Traditional budgeting uses annual budgets – one fixed plan for the whole year - as the basis for target setting, planning and control. By contrast, rolling forecasts are continuous updates based on the best information currently available. Regular forecasting helps to quantify, and by implication manage, the gap between the original approved budget and the forecast. We, accountants, believe that forecast accuracy can be improved through analysis, learning, judgement, detailed supporting data and good systems. We analyse data to understand internal and external trends and are able to model a range of forecast options. We make plans so we have plans to change.

Rolling forecasts, we are told, facilitate event-driven planning and responsive resource allocation. The conventional wisdom tells us that regular forecasts can help us to understand and manage risk. 

But how? There are many types of risk. In 2017 our countries, our businesses and we as individuals will be challenged to deal with a spectrum of risks that range from the daily risks of living that we take for granted, to risks we don’t know about and can’t even imagine. A single estimate of sales, costs, and profits is based on choices and probabilities which are but one view of a broad range of possible futures.  


In spite of this, it is my experience that many senior managers still want to work with one number, a single view of the budget or forecast (which they hope is reasonably accurate).
In this world of uncertainty, finance departments must invest time and effort to understand and classify risk, make risk explicit, and find ways to estimate the reliability of the forecast. 

“Large, long-lived, and successful organizations may be more prone to misclassifying risk and uncertainty. Years of success can lead to a culture where conventional wisdom rules and leaders do not want to be questioned. If a risk is not understood, classified and debated, and if data and analytics are not explicitly part of decision-making, the organization may falter. 

Businesses need to create a culture of healthy dissent, fact-based analysis and decision-making to identify all potential issues, implications, and actions. Unchanging strategies and tactics work until they don’t, sometimes with disastrous outcomes.” (See Note 2)

The South African grocery retailer Pick ‘n Pay reported declining profit margins and returns for 4 years in a row between 2010 and 2013, before remedial action was taken.
Prior to the invention of the digital camera, and for some years thereafter, Kodak didn't foresee the risks to their traditional business and eventually filed for bankruptcy in 2010.

Accountants need to communicate the risks in a budget or forecast to their managers.
It is, therefore, critical that any budget and forecast should be supported by a detailed commentary on the risks implicit in the numbers, in four areas:

  • the estimated accuracy of the forecast and its upper and lower limits 
  • explanation and classification of the relevant downside and upside risks and uncertainties. 
  • trends in technology, products, customer wants, and monetary trends in sales volumes and prices, raw material prices and other input costs
  • alternative outcomes and scenarios                           


Forecast accuracy is unlikely to be consistent throughout the forecast. Thus a single point figure of the reliability of the forecast (“this forecast is 90% reliable”, for example), is misleading. An estimate of reliability should be provided for each phase or time period of the forecast. 

Project managers describe a learning process known as the “cone of uncertainty”. At the beginning of a project, comparatively little is known about the project, and so forecasts are subject to large uncertainty. Uncertainty decreases over time. As more is learned about the project, the uncertainty tends to decrease, reaching zero at the end of the project. 


Cone of uncertainty. Source: Wikipedia

Similarly, forecast uncertainty increases as we move further into the future. We are usually able to attach greater confidence to the forecast for the month or quarter immediately ahead, than for the month or quarter 12 months ahead.

The forecast period within the “cone of certainty” will influence the method of forecasting and the information which is used. Refer Appendix 1 for an industry-specific example.  


The American politician Donald Rumsfeld was once asked: “Does the government of Iraq have weapons of mass destruction which it can supply to terrorist groups?” 

He replied “There are known unknowns. That is to say, there are things that we know we don't know. But there are also unknown unknowns. There are things we don't know we don't know.  And if one looks throughout the history of our country and other free countries, it is the latter category that tends to be the difficult ones.” 
Donald Rumsfeld 2002. Source: Wikipedia
(See Note 3)

“Known unknowns” refer to risks you are aware of, such as a major contract being cancelled. We try to understand these by identifying the upper and lower limits of each item, the probability of occurrence, and the past and likely future frequency.

Unknown unknowns are risks that come from situations not thought of, or have not been considered possible, or are so unlikely that they have never been considered worth forecasting. Mathematician Nassim Taleb uses the term “black swans” to describe these rare unplanned events with severe impact that, with hindsight, were predictable or explainable. Prime political examples in 2016 were Brexit and the Trump presidency. Black swans remind us that there are limits to knowledge when it is based only on observations and past experience. (See Note 4)

The “known unknowns” and how they have been dealt with in the forecast must be made explicit in the commentary. 


Accountants and economists use trend data to try to uncover and convert uncertainty into known risk. We believe that “the trend is your friend.” (See Note 5). We use our understanding of trends to project the past into the future. For example, we can usually justify our assumptions about demographic shifts and certain new technologies.

Trends in technology, products, and customers, should be identified with an explanation of how they have been dealt with in the forecast.
Similarly, monetary trends in sales volumes and prices, raw material prices and other input costs should be highlighted.
Some trends such as technological change and new product sales may be exponential rather than linear and thus more difficult to forecast.


A single figure forecast reflects the preparer’s view of a likely outcome, based on choices from a multitude of alternatives. The forecast presentation should include a discussion of alternative outcomes and scenarios.  

In scenario planning, we combine assumptions, trends, knowns and unknowns into a limited number of permutations that speculate on alternative views of the future. 

We make assumptions about factors that will affect the nature of the future world in which the organisation operates. Examples are demographic shifts, new technologies, future interest rates, outcomes of political elections, rates of innovation, fads and fashions in markets. 

These factors are analysed to understand how these major drivers for change will impact on key assumptions about the future.  These are used to generate a wide range of scenarios which are then reduced to 2 or 3 most likely scenarios which are evaluated in detail.


In an uncertain and fast-changing world, line managers need to be made aware of the uncertainties and risk inherent in the financial forecasts provided to them. Uncertainty is difficult to manage but uncertainties can be converted into known risk as forecasting capabilities and data management improve. When risk is understood, it can be categorised, mitigated, managed, hedged or even avoided. Uncertainties require continual review to identify changing facts and circumstances that affect risk. In the future, winning organizations will have a greater ability to identify, understand and manage risk.

Note 1: A question of timing?
Answering critics of the bank’s gloomy November forecast for the economy, their Chief Economist admitted that the bank did not anticipate the resilience of consumer spending after Britain voted to leave the EU. But he said that he thought the bank was wrong about timing not about the fundamentals, and that the Bank of England still expected Brexit to harm growth.

Note 2: Reference 
This quote is from an excellent article “Big Data: Blurring risk and uncertainty” by Bill Pieroni, chief executive officer of Accord, featured in LinkedIn Technology, August 22, 2013.

Note 3: The “unknown knowns”.
Beyond these three categories, there is a (psycho) logical fourth, the “unknown known”, where people are in denial about (or intentionally refuse to acknowledge) facts and issues they subconsciously know to be true. Rumsfeld thought that the main dangers in the confrontation with Iraq were the 'unknown unknowns', that is, the threats from Saddam which the United States suspected but could not prove. But often the main risks lie in the ‘unknown knowns’ - the moral values, beliefs, suppositions and practices people disavow and pretend not to know about. Did the US know there were no weapons of mass destruction in Iraq?

Note 4: Adapting to “black swans”
A colleague in the Reinsurance Industry suggests that many of these so-called once-in-a-lifetime, uncertain events actually occur with regular frequency and potentially knowable impact. Some Black Swan events occur every few years (earthquakes, tsunamis, stock exchange crashes) while others are separated by longer, yet regular, time intervals. The regularity of these events suggests that black swans are giving way to shades-of-grey swans. The Reinsurance Industry continuously monitors statistical data to identify, model, and mitigate black swan risks.

Note 5: The trend is your friend
“Extrapolating from the past is like driving down the road looking in the rear view mirror... fine until you come to a bend in the road”  (Toby Wilson, Microsoft Financial Director, UK.)

Appendix 1: Suggested sources of information for rolling forecasts in different time periods

Around the World, FP&A Goes by Many Names

In an interview to GTNews, Larysa Melnychuk, Managing Director at FP&A Trends group, shares her knowledge on the current state of Financial Planning and Analysis in different countries.

Although it is becoming commonplace to refer to financial planning and analysis as FP&A, this is actually an American term and one that is still not widely used outside of the US.

It is true that around the world, FP&A goes by many names.

UK & Europe

Larysa Melnychuk, the founder of the London FP&A Club, notes that five or six years ago in the UK few people would have recognised the term. “I have worked in FP&A in the UK for the past 16 years, but it wasn’t until about five years ago that I began to hear the term,” she says. “Even now here in the UK, you will find finance directors at medium-sized companies who have never heard of FP&A.”

Instead, Melnychuk finds FP&A is referred to by several different names, including:

  • Management accounting
  • Business finance
  • Commercial finance
  • Decisions support
  • Budgeting and planning
  • Business planning and analysis (BPA).

“FP&A is still perceived as a very American term in the UK,” Melnychuk adds.

As for the chain of command, up until about five years ago, many FP&A departments in the UK reported to the financial controller. “For some companies this worked and others it didn’t, but they all seem to say the same thing: financial controllers look at historical information. They’re not forward-looking,” Melnychuk says.

Today, that trend is changing. Much like the US, the FP&A function now more often reports to the chief financial officer (CFO) in the UK. This allows for FP&A to be more strategic and influential across the organisation. However, there are still some companies where FP&A reports to the financial controller. A best practice for companies, Melnychuk suggests, would be to have FP&A report to the CFO, because FP&A and the financial controller often require different skills and serve different purposes.


In Europe, how companies refer to their FP&A departments often depends on how international they are. Typically, the term FP&A is only used by companies that work regularly with the US.

Generally, in Nordic countries, as well as in Germany, France and the Netherlands, FP&A is typically referred to as business control. Business control is not to be confused with finance control, Melnychuk stresses. “Finance control is everything related to financial reporting and statutory accounting,” she says. “Business control is part of management accounting, which includes FP&A.”

Unsurprisingly, the titles for FP&A professionals in Europe also differ from their American counterparts. FP&A managers are often referred as business controllers in some European countries.

However, Melnychuk adds that in Nordic countries and in Germany she’s met business controllers that do not consider themselves finance people. “One factory and plant controller described to me his role as operations rather than finance related,” she says.

For FP&A individuals to become more strategic and more influential, the best course of action for them is to report directly to the CFO, and practitioners are recognising this. “We see indications that this is exactly what is going to happen over time,” Melnychuk says.

While the terminology for FP&A might differ from country to country in Europe, other things typically remain consistent. Medium-sized companies with revenues less than US$1bn may have combined functions in finance, but most large companies have a separate FP&A/business control/management accounting (etc.) department.

Middle East

FP&A in the Middle East differs from Europe and the US. Even some of the largest multi-billion dollar companies are still family based and – unlike Europe – their finance departments are not divided into multiple factions. As such, many staff members have combined functions, so they may work in both FP&A and finance reporting.

“Traditional companies in the Middle East do not actually know what FP&A is; they’ve never heard of it,” Melnychuk says. “For example, the finance manager will simply be responsible for budgeting and planning, as well as statutory reporting.”

Given that FP&A essentially does not exist as its own department many local companies in the Middle East, it is difficult to determine any common reporting structure for FP&A. Finance staff, of course, report to the CFO, but until companies in the Middle East begin to establish FP&A as its own entity, there is no clear picture of a reporting structure.


 This article was first published on www.gtnews.com 

Forecasting: Should the Mean APE Rule the Accuracy Planet?

By Hans Levenbach, PhD, CPDF author of Change & Chance Embraced

Hans Levenbach, PhD is Executive Director, CPDF Training and CertificationProfessional Development Programs. He conducts hands-on Workshops on Demand Forecasting for multi-national supply chain companies worldwide. He is group manager of the LinkedIn groups (1) Demand Forecaster Training and Certification, Blended Learning, Predictive Visualization, and (2) New Product Forecasting and Innovation Planning, Cognitive Modeling, Predictive Visualization.

He invites you to join if you have interest in sharing conversations on these topics.

LinkedIn profile: Hans Levenbach

Planners and managers in supply chain organizations are accustomed to using the Mean Absolute Percentage Error (MAPE) as their best (and sometimes only) answer to measuring forecast accuracy. It is so ubiquitous that it is hardly questioned. I do not even find a consensus on the definition of forecast error in supply chain organizations around the world among practitioners who participate in the forecasting workshops. For most, Actual (A) minus Forecast (F) is the forecast error, for others just the opposite.

Among practitioners, it is a jungle out there trying to understand the role of the APEs in the measurement of forecast accuracy. Forecast accuracy is commonly measured and reported by just the Mean Absolute Percentage Error (MAPE), which is the same no matter which definition of forecast error one uses.

Bias is the other component of accuracy, but is not consistently defined, either. For some, Actual (A) minus Forecast (F) is the forecast error, for others just the opposite. If bias is the difference, what should the sign be of a reported underforecast or overforecast? Who is right and why? 

Outliers in forecast errors and other sources of unusual data values should never be ignored in the accuracy measurement process. For a measurement of bias, for example, the calculation of the mean forecast error ME (the arithmetic mean of Actual (A) minus Forecast (F)) will drive the estimate towards the outlier. An otherwise unbiased pattern of performance can be distorted by just a single unusual value. 

When an outlier-resistant measure is close to the conventional measure, you should report the conventional measure. If not, the analyst should check out the APEs for anything that appears unusual. Then work with domain experts to find a credible rationale (stockouts, weather, strikes, etc.)

Are There More Reliable Measures Than the MAPE?  

The M-estimation method, introduced in Chapter 2 of my new book can be used to automatically reduce the effect of outliers by appropriately down- weighting values ‘far away’ from a typical MAPE. The method is based on an estimator that makes repeated use of the underlying data in an iterative procedure. In the case of the MAPE, a family of robust estimators, called M-estimators, is obtained by minimizing a specified function of the absolute percentage errors (APE). Alternate forms of the function produce the various M-estimators. Generally, the estimates are computed by iterated weighted least squares.

It is worth noting that the Bisquare-weighting scheme is more severe than the Huber weighting scheme. In the bisquare scheme, all data for which | ei | ≤ Ks will have a weight less than 1. Data having weights greater than 0.9 are not considered extreme. Data with weights less than 0.5 are regarded as extreme, and data with zero weight are, of course, ignored. To counteract the impact of outliers, the bisquare estimator gives zero weight to data whose forecast errors are quite far from zero.  

What we need, for best practices, are robust/resistant procedures that are resistant to outlying values and robust against non-normal characteristics in the data distribution, so that they give rise to estimates that are more reliable and credible than those based on normality assumptions.

Taking a data-driven approach with APE data to measure precision, we can create more useful TAPE (Typical APE) measures. However, we recommend that you start with the Median APE ( MdAPE) for the first iteration. Then use the Huber scheme for the next iteration and finish with one or two more iterations of the Bisquare scheme. The Huber-Bisquare-Bisquare Typical APE (HBB TAPE) measure has worked quite well for me in practice and can be readily automated even in a spreadsheet. This is worth testing with your own data to convince yourself whether a Mean APE should remain King of the accuracy jungle!!

Details may be found in Chapter 4 of Change & Chance Embraced: Achieving Agility with Demand Forecasting in the Supply Chain.

The Triple A of Art, Artificial Intelligence & Actuals vs Budget

By Timo Wienefoet, Managing Partner at IMPLEXA GmbH

Hito Steyerl was recently crowned the most powerful contemporary artist by ArtReview. The Professor at the renowned UdK Berlin cofounded the UdK Research Centre for Proxy Politics. Her latest works at the Documenta and the Skulptur Projekte continue to dissect the digital world. It was a critique of her that inspired to span the bridge from Arts to Artificial Intelligence (AI) to Corporate Planning and Analysis:

“Statistics have moved from constructing models and trying to test them using empirical data to just using the data […] They keep repeating that correlation replaces causation. But correlation is entirely based on identifying surface patterns, right? The questions–why are they arising? why do they look the way they look? – are secondary now. If something just looks like something else, then it is with a certain probability identified as this “something else,” regardless of whether it is really the “something else” or not.” (Hito Steyerl, full article)


AI-driven decision as  a "Black  Box"

The indication that AI-driven decisions are merely understood is widely discussed. Two examples focus on the societal effects and responsibility for

  • Social media in creating Echochambers– the algorithmic incarnation of the confirmation bias to generate advertisement revenues - and handling hate speech,
  • Compensation claims on machine learned driverless cars involved in accidents.

How can the risks these algorithms post be economically grasped if the algorithm is understood as a black box immune to insights and legal claims? This is one question to ask in the coming planning cycles when these concepts are to be integrated into the value chains.


Can the Technology-Driven Methods Bring Value to FP&A?

Another aspect is considering AI in the FP&A process itself. The AFPs Survey on Budgets ranks the logistics team last in “value perceived from budgeting”. Logistics is the corporate unit most exposed to AI. The communicative, non-deterministic aspects of corporate planning are undervalued when planning mainly concerns dead matter. Key question is, can their technology-driven methods bring value to the very lively corporate budgeting and forecasting? They can and they should because AI provides for the basic heuristics evolution prove most fit. This includes the misuse like the German saying to use a cannon to shoot a sparrow “mit Kanonen auf Spatzen schiessen”.


AI in FP&A: Exploration-Exploitation Tradeoff

One intersection of artificial intelligence and budgeting is the exploration-exploitation tradeoff. Exploration is the acquisition and collection of data while exploitation is making use of it. The selection of a casino machine involves this consideration. Explore by using coins on many machines, exploit your lucky one until the luck leaves. The conundrum lies in the interaction of the terms: continued exploration requires reevaluation of the exploitation. Two valuable FP&A insights from the tradeoff emerge: there is no optimal strategy and the timeframe is crucial.

The next cycle resets the existing one, makes it obsolete. Additionally, the timeframe of the corporate plan exceeds most Las Vegas stays. This holds true for the complexity of the planning scope. Compared both to choosing “your” machine a casino hall. The lesson can be applied to investment decisions as decisions on exploitation. The decision process should reflect explorative changes rather early than late. A strong argument for the Rolling Forecast where the near future weighs heavier than the distant one. One AI representation reflecting this is called “Least Regret”. It converts the valuation form maximizing future potential gains to minimizing future regret. Least Regret is not about chasing the best, but about preventing the worst. Regular investment reviews and divide & conquer approaches to big projects are representations of this method. Exploitation must start to yield results, although in a month – don’t make it a year – exploration may a turning tide.


Neomania in FP&A

People over-explore. The former “quant” Nassim Taleb described Neomania as focus on “to be justified innovations” at the expense of proven methods. The concept was described in detail as part of the not so obvious facets on budgeting and forecasting. Does AI qualify for over-exploration? It is a humane survival instinct, as the unknown options have a strong upside with limited downside. The math behind the bias was proven by the Operations Research Professor John Gittins from Oxford University. Gittins index structures decisions on which path into the unknown to take. It helps to identify the exploitable casino machine. The index is calculated out of the available explored information of each option/ machine. Gittins embedded a concept called discounting of future rewards. That concept sounds familiar to FP&A. The results apply well to gambling: a winning turn urges to stay, a streak justifies a couple losses before switching to more promising machines the gambler knows less about. It shows, there is value in finding out. Results are suboptimal with switching cost and timely calculation requirements, which depict the main requirements in Capital Expenditure decisions: well calculated, because of high costs in switching. Flexibility by design diminishes this. One example of a regulatory induced flexibility is the Data Portability concept in the European Data Protection Law. It seems to improve chances if a vendor selection goes sour.

Two options are cited to optimize under the mentioned limitations: the first is the mentioned Least Regret. The second one is the simplification. Simplification as an alteration to Gittins Index skips discounting future rewards calculated out of past information. It focusses solely on the potential of the new. These upper confidence bound algorithms are a perfect metaphor for Neomania. The emphasis is on exploration, the unknown strongly favored. Incubators, Accelerators and Break Outs are organizational means to harness the next big thing hidden in plain sight. A stringent funding methodology coupled with experience should navigate these means into the drawer or out into the business world.


Combining the Now and the Future requires a well-prepared FP&A team

In general, the explore-exploit offers more practicable insights to FP&A:

  1. For the past, good teams stay away from exploring late in the game and exploit the best option. For FP&A it then is to lay out the efficacy of handling the restless reality of past decisions telling the story via the financial statements.
  2. The reality of tomorrow is best prepared for with a restless mind that exploits late while exploring the less mature fields. The reality assigns 90% of FP&A to pursue the first, omitting the potential to better understand the future economically.
  3. Artificial intelligence tackling the explore-exploit tradeoff so far has not and is believed to never will be able to solve this dilemma.

Future coordination is a "brainer "- not a no-brainer. Combining the Now and the Future requires a well-prepared FP&A team. AI as Robotic Process Automation (RPA) can support with the “no-brainers”. Looking ahead requires diligence and thinking time, which RPA can provide space for. Also, computer algorithms prescribe to keep exploring for future gains. This includes looking to the Arts and the Artificial Intelligence developments.