The office of the CFO is often the most data-driven functions in a business, comprising not only financial data but other performance indicators of the business, leveraged to report, forecast and optimise business operations. It lends itself naturally to software applications and numerous marquee softwares exist in the space such as NetSuite (Oracle), QuickBooks (Intuit) and Anaplan.

In this paper, we break down the finance organisation and identify sub-sectors that are ripe for dedicated software applications. We deep dive into the rationale for enterprise spend in these sub-sectors and the current challenges that are top of mind for the office of the CFO.

The SaaS Startup Finance Function (ILLUSTRATIVE)

The Enterprise Finance Function (ILLUSTRATIVE)

While enterprise finance functions can vary depending on company structures and business needs, departments quite often features the above categories:

• Accounting and Financial Controls
• Financial Planning & Analysis (FP&A)
• Investor Relations
• Procurement
• Tax and Compliance
• Treasury
• Corporate Development
• Analytics / BI

Other sub-teams or sub-verticals that can be part of the Office of the CFO:

• Governance, risk and compliance (AML)
• ESG reporting
• Billing and operations

For the purpose of this research, we have chosen Financial Planning and Analysis and Tax, namely Transfer Pricing, as material and often greenfield market opportunities with enterprise software applicability.

1. Financial Planning & Analysis (FP&A)

1.1. What is FP&A?

FP&A is responsible for a range of activities, such as planning, forecasting, budgeting, and conducting analyses that support critical business decisions and ensure a company’s financial health. This can involve preparing management reports, creating organisational budgets, developing long-term planning models, and performing ad-hoc analyses for executive leadership.

However, the role of FP&A can vary significantly based on the company’s size. In traditional SMEs and startups, FP&A typically focuses on cash flow management and forecasting. In scale-ups and larger organisations, FP&A involves creating long-term strategies by leveraging data from BI tools, data warehouses, accounting systems, HR platforms, CRMs, and other sources to build financial forecasting models.

Overall, FP&A functions are primarily driven by:

• A robust calculation engine: FP&A teams rely heavily on Excel (for forecasting and budgeting); or SQL queries for dealing with larger volumes of data.
• Data gathering across the organisation: FP&A relies heavily on gathering data (often manually) and working closely with Go-To-Market (GTM), Product & Engineering and Administrative teams (e.g., HR, Legal, etc.) to create accurate revenue forecasts and determine the necessary spending to meet business targets.

1.2. Market opportunity

FP&A gained greater importance after the financial crisis, as shown in a 2009 McKinsey survey. In 2020, McKinsey also highlighted the importance of scenario-building and the higher responsibility of the FP&A team during the Covid-19 crisis. With the heightened uncertainty and volatility caused by several global challenges (Covid-19 pandemic, Ukraine War, inflation, supply chain disruptions), FP&A is set to play increasingly important role in helping businesses manage risks and anticipate various economic outcomes, allowing for more agile decision-making.

For these reasons, finance executives are also increasing spending in FP&A tech – albeit this is still largely dominated by outdated, legacy tech. The Global FP&A was valued at USD 3.7 billion in 2022 and is projected to reach USD 16.9 billion by 2030, growing at a CAGR of 16.60% from 2023 to 2030.

1.3. Common FP&A Pain Points

Recent surveys have highlighted several recurring challenges faced by finance leaders, which can be classified as follows:

• ⛔No unified source of truth for data and metrics: A major issue is data fragmentation due to the proliferation of systems containing financial data (e.g., ERP, data warehouses, Salesforce, billing platforms). This makes even basic metric calculations difficult, as data from different systems rarely align seamlessly.
• ⛔Lack of real-time data access: Legacy tools typically require finance teams to wait for the accounting close at month-end, leaving them to make educated guesses about their revenue, expenses, and cash flow during the month or quarter without access to real-time financial data.
• ⛔Non-finance-grade systems: Revenue data is often stored in platforms like Salesforce, but since Salesforce is not designed as an accounting or finance tool, it lacks key controls such as audit trails and change reports, which are necessary for tracking metrics like ARR (Annual Recurring Revenue) on a day-to-day basis.
• ⛔Need for data science expertise: Many companies use tools like Looker for reporting, but finance teams increasingly require advanced data skills. For instance, hiring SQL experts or undertaking training in SQL is becoming essential as data management and analysis become more complex within finance functions.
• ⛔Manual reconciliations: Weekly and monthly reconciliation processes are often still handled manually, which can be time-consuming and prone to errors, slowing down financial reporting and decision-making.

These challenges illustrate the growing need for better tools, real-time data access, and specialized skills to improve efficiency and accuracy in FP&A.

1.4. The role of software in the space

Software – Status Quo

Currently, the FP&A (Financial Planning & Analysis) market is dominated by legacy technologies that are often expensive, difficult to implement, and cumbersome to use. As a result, many companies still rely on Excel for forecasting and budgeting. Here’s an overview of the existing generations of FP&A software:

• First Generation: Tools like SAP and Oracle Hyperion ran on-premise and were incredibly expensive and time-consuming to implement. These systems were out of reach for many organisations, particularly those that were smaller or less flexible.
• Second Generation: Tools such as Anaplan and Adaptive (Workday) are cloud-based, making them more affordable and quicker to deploy. However, these tools still became known for their complexity, with Anaplan introducing new syntax unfamiliar to Excel users, and pricing gradually increased over time.

Software – New generation

A new wave of FP&A tools is emerging to address the needs of scale-ups and larger businesses. These tools either aim to replace Excel or function as a layer on top of it, integrating with various software platforms such as accounting systems, HR tools, and CRMs, and are capable of handling large volumes of data. This has created significant opportunities for disruption, with 3rd-generation FP&A tools already beginning to challenge the status quo.

In the realms of forecasting and budgeting, there are three key areas where these new tools are driving innovation:

• FP&A: These tools enable organisations to run flexible scenario analyses and build detailed financial forecasts, setting revenue and spending guardrails for upcoming fiscal periods. Notable companies in this space include Mosaic, DataRails, Abacum, and Pigment.
• Cash Flow Management: These platforms integrate with a company’s bank accounts and financial systems to track cash inflows and outflows in real time, offering improved visibility into the cash position. The rise of open banking APIs has enabled the development of automated cash flow management tools that aggregate transaction data and present key metrics for finance teams. Examples of companies in this space include Agicap and Trovata.
• Headcount Planning: With headcount often accounting for 70-80% of total operating expenses, these tools help organisations plan their hiring to meet strategic and operational goals while staying within budget. They offer features such as organisational charts, diversity reporting, and compensation management. Companies like ChartHop and Knoetic are leading in this area.

Where do we see differentiation?

The companies that we believe will be successful are those that built products that FP&A truly needs, not just a big, expensive modelling tool. Generally, we believe successful tools will have a combination of the following:

• Usability: leveraging a modern, intuitive UI, and comprehensive training and documentation to see rapid time to value
• Depth of (pre-configured) integrations: providing a powerful product able to integrate with multiple data sources to create a single source of truth – e.g. Abacum’s no-code data transformation or Mosaic’s pre-configured metrics
• Ease of implementation: making it as self-serve as possible, adaptable and customizable
• Collaboration features: by enabling version control, individual tracking, tracking and approval flows
• AI/Automation capabilities: AI takes this to the next level to prepare decks, generate summaries of complex financial reports to share with non-financial departments, or let staff question their data using Natural Language

What role do we see for AI in the space?

AI has the potential to revolutionise FP&A by automating tasks, improving forecasting, and enabling data-driven decision-making. We are already starting to see tools like Pigment, Abacum integrating AI into their FP&A solutions.

While we recognise the potential of AI to add new use cases in FP&A, AI adoption can also faces a number of barriers:

• Behavioural Resistance: Finance teams often prefer manual forecasting and may distrust AI tools, or feel professionally threatened.
• Data Quality Issues: AI relies on clean, integrated data, which many organisations struggle to maintain.

Despite these barriers, the future of AI in FP&A is promising as data centralisation improves, AI tools integrate better with existing workflows, and finance teams become more comfortable with AI augmenting their work. Over time, AI adoption will accelerate, driven by its potential to enhance efficiency, accuracy, and strategic decision-making.

Potential adoption avenues for AI within FP&A

AI could provide benchmarks during planning, offering insights on aggregated spend ratios or even granular line items. It could also recommend potential savings and renegotiation opportunities for key expenses. For instance, in enterprises heavily reliant on cloud services, AI could assist dedicated FP&A professionals in managing vendor negotiations more effectively within their FP&A tools. Furthermore, AI could model extreme scenarios, such as stress-testing business resilience under highly disruptive circumstances or suggesting the maximum potential of the business given available resources

2. Transfer pricing (TP)

2.1. What is transfer pricing?

Transfer pricing (TP) refers to the practice of setting prices for goods, services, or intellectual property transferred between related entities within a multinational corporation. This typically involves transactions between subsidiaries, branches, or other affiliated companies operating in different countries

The main purposes of transfer pricing are:

1. Allocating profits and costs among different parts of a company
2. Determining the taxable income for each entity involved in cross-border transactions
3. Ensuring fair pricing that complies with tax regulations

Transfer pricing is a critical aspect of international taxation and corporate strategy. Tax authorities closely scrutinize these transactions to prevent companies from manipulating prices to shift profits to low-tax jurisdictions and minimize their overall tax burden.

To maintain fairness, most countries require that transfer prices be set according to the “arm’s length principle,” which means the price should be comparable to what would be charged between unrelated parties in a similar transaction.

“10 years ago, transfer pricing was one-ended, you looked at a given branch in a certain country and taxation was dedicated to this, not much oversight of the full (global) picture” – Tristan Noyes, B-Flexion

Larger companies with ~$1B+ in annual revenues will likely have a small in-house transfer pricing team, but they will still engage with consultants (notably the Big4 who have international presence).

Mid-sized companies with international presence will likely engage with consultants to prepare relevant transfer pricing documentation assuming no in-house team and the finance teams would not necessarily be tax specialists.

In-house TP teams are quite small but interact with Big4 frequently, some examples of ways they might work together include:

• Want to put a loan in place between two entities, need financial sources to benchmark these, Big4 can quote a price for setting this up – bespoke use cases where in-house teams don’t have the resources
• Might launch a new product in a number of new jurisdictions and have an initial idea in the head on how to structure it but want an advisory perspective to understand if there are any risks with the setup described or any local country concerns
• Documentation – might in-source some of it and use a system internally but country files for some countries may be easy and for some one may want a Big4 stamp on the file for audit purposes
• Dispute resolution – any issues arising from audits or inquiries from tax authorities

In-house team setups

• Transfer pricing is a finance function, with control needed at the point where invoices are raised between two parties within the company (or intercompany).
• While many organizations benefit from internal competition, this can lead to significant time waste in price negotiations. From a transfer pricing perspective, a simple benchmarking analysis is typically sufficient, making it challenging to justify and document extensive negotiations.
• Operational transfer pricing is difficult to assess from an advisory standpoint as it’s managed in-house. The finance function determines the pricing that flows through the accounts and processes transfer pricing adjustments to achieve the correct profitability.
• A dedicated transfer pricing team isn’t always necessary, as it may duplicate functions within finance and potentially waste tax expertise.
• Companies are increasingly moving towards a global finance operations hub or considering an intercompany team, which acts as a control center or arbitrator between different parts of the organization.

2.2. Market opportunity and regulation

• All multinational companies are affected by transfer pricing
• In 2018, the OECD commented that all the world’s MNCs, over 80,000 companies would be subject to transfer pricing
• We conservatively estimate that an MNC could spend at least $100,000 per annum on transfer pricing software
• We also know that B-Flexion spends approximately £50,000 for consultancy for reporting purposes for one entity
• Transfer pricing kicks in for two or more entities as and when profit allocation matters
• Enterprises have dozens if not hundreds of entities and sometimes even entity numbers in their thousands

80,000 companies spending $100,000 would imply a market size of $8 BN

Within tax policy, the OECD considers Base Erosion and Profit Shifting (BEPS) as a major issue, the framework was first proposed at a meeting in Kyoto, Japan in June 2016.

Base erosion and profit shifting (BEPS) – where multinationals shift profits to low or no-tax locations where they have little or no economic activity or erode tax bases through deductible payments like interest or royalties – costs countries USD 100-240 billion in lost revenue annually. That is equivalent to 4-10% of global corporate income tax revenue. Although some BEPS schemes are illegal, most are not.

There are two “pillars” to BEPS; Pillar One refers mainly to profit allocations and Pillar Two more globally is trying to address a global minimum corporate tax threshold, though the later is not in force.

Most geographies subscribe to the OECD guidelines, at time of writing this is over 145 countries and jurisdictions. The guidelines can be summarised into 15 BEPS actions. The most relevant to reporting requirements are:

• Action 8-10: Aligning Transfer Pricing Outcomes with Value Creation
  • More emphasis on accurately delineating transactions
  • Guidance on risk allocation and intangibles
• Action 13: Transfer Pricing Documentation and Country-by-Country Reporting
  • Introduction of a three-tiered approach to documentation: Master File, Local File, and Country-by-Country Report
  • Master File: provides an overview of a group through a transfer pricing lens
  • Local File: analyses and explains why the arrangements at an entity level are consistent with the arm’s length standard
  • Country-by-Country Report: shares information on an MNC’s global allocation of profit, taxes paid and indicators of economic activity among the countries in which they operate

“It was an overhaul of the TP space at the time, historically it was all down to legal forms, right paperwork could help with loopholes within a defendable reason – moved everything to a substance over form with much clearer guidelines; ‘aligning profit with value creation.’” – Thomas Jepson, TP Expert, Johnson Matthey

2.3. The role of software and services in the space

The Big4 (namely PwC, Ernst & Young, Deloitte and KPMG) have full professional consulting and service offerings, as do smaller, full-service accounting and tax advisory firms.

On tooling, some of the Big4 have naturally developed their own softwares over the years, e.g. Deloitte TP Digital DoX and PwC’s TP Architect:

• These tools often underperform and are frequently replaced by newer versions, with some providers disappearing from the market entirely.
• Many clients have had negative experiences with these tools, as they often fail to fulfill promises. The process of establishing transfer pricing documentation on one tool, only to migrate to another, involves substantial manual effort.
• This issue is particularly prevalent with internally developed tools, such as those created by each of the Big Four accounting firms, which have had to replace their tools with newer versions.
• These tools are often designed as all-encompassing solutions, but a simpler tool would be more effective than a complex one where users only utilize 20-30% of the available features.

Consultants also do custom tooling (or macros and processes) for clients and/or implementations of other software solutions.

“At Deloitte, I worked on a new type of project to speak with the finance manager in each jurisdiction [of a client] where each month they were doing a calculation of their profitability in Excel, all formula-driven, taking a day or so to calculate what the profitability should be, what it was and what the go-forward adjustment should be. That’s a day a month for a finance manager out of 20 days; this is resource-intensive – Deloitte created through the tax ventures team a macro-enabled .xlsx workbook with ML to categorise costs, all built together in a bespoke way to automate the calculation.” – Thomas Jepson, TP Expert, Johnson Matthey (ex Deloitte)

Software – Benchmarking

Comparables and benchmarks are often used to determine fairness when establishing arm’s length transfer pricing between entities. This is an obvious workstream in TP where AI can add value, for example at https://www.armslength.ai/

Other benchmarking solutions are available through data vendors such as Thomson Reuters, Bureau van Dyk, Moody’s etc.

Software – Reporting

Much as with financial reporting or accounting software, a wave of tax reporting software has also emerged over the past 10 years, in line with regulatory developments. Large multinational companies need to produce Master files, Local files and Country-by-Country reporting for tax authorities.

While some needs might be simple enough to run out of a spreadsheet, others may benefit from software.

Software – Operational TP

Whereas reporting softwares for TP are more front-end, operational TP softwares can be considered more back-end and day-to-day intercompany transactions, to transform data through formulae into tax formats sometimes even within ERP systems. ERPs like SAP and Oracle offer their own operational TP modules, though they are often criticised for lacking depth and appropriate structures. MS Excel is still the dominant tool for operational TP work.

2.4. Summary of software providers in the space

• Most providers in the space who claim to have software either began as a professional services or advisory business or built software to better enable their advisory business
• Cluster of newer providers in Amsterdam and Nordics
• These providers consider the Big4 and similar professional services organisations (e.g. BDO, Grant Thornton) as competition
  • Exactera: “No cooperation with the big 4 they were very much their competition. The goal was to migrate their business to their platform. If it was easier to sell a smaller scope at first, they would do that and then expand. But no partnerships with the big 4, it was strictly competition” – Source: Expert call with Andrew Dorfman (ex Exactera VP Customer Success)
• US clients don’t want another technology that they need to manage and consumer more reporting services, whereas European clients want more control over the process and more inclined to take on the documentation themselves
  • Exactera: “We found that customers in Europe were more inclined to utilise the software whereas US customers would want to use services” – Source: Expert call with Andrew Dorfman (ex Exactera VP Customer Success)

General trends in TP

• Advisory work in transfer pricing is becoming increasingly sophisticated. While early career professionals may focus on basic TP reports, the future of the field will shift away from routine report writing (except in special cases).
• TP professionals need to develop more advanced skills to handle complex cases.
• There’s growing reliance on software tools and AI for support.
• Database searches for benchmarks now involve more nuanced inclusion and exclusion criteria.
• A recently implemented AI-based tool assists in preselecting business descriptions, a task previously done by students or graduates but now enhanced by AI support.

What role do we see for AI in the space?

Transfer pricing work, namely compliance documents can be templatized and repeated which lends itself well to generation of reporting from structured data into formatted output.

Benchmarking analysis which is a requisite to determine arms length principles can be sped up with the use of AI to generate benchmarks and better select them and report on their selection for given transactions.

Intercompany transactions can benefit from AI for optimisation and recommendation in the long-run, though this requires operational TP to be in a more structured data format first, before anything strategic cane take place.

Is the space ready for AI?

The most immediate opportunity presents itself in benchmarking, which is largely performed by advisors on behalf of MNCs.

Compliance documents and regulatory filings performed by advisors may warrant a stronger use case than in-house with MNCs due to breadth of reporting, versus needing perhaps only one template, for example, for a local file within an MNC.

For AI-driven strategic TP to take place, operational TP needs software adoption for structured datasets first, and this presents a big opportunity.