The Tax Function of the Future - PwC · Tax Function of the Future series ‘Tax Analytics’—The...

The Tax Function of the Future Focal Point on ‘Tax Analytics’—The next frontier

Tax Function of the Future series

November 2016

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‘Tax Analytics’—The next frontier

Tax Analytics—a new capability for Tax functions—is gaining momentum as it promises to expand greatly the Tax function’s ability to deliver increasingly critical insights and value-added benefits to the enterprise.

Tax Analytics can empower a Tax function to….

• View multiple years, periods, and scenarios of data at the touch of a button

• Visualize global changes to legal entity structure, trading partners, and tax return filings

• Quickly evaluate all information needed for repatriation strategies

• Track customized key performance indicators

• Review ‘what if’ scenarios for potential legislation

• Automate decision-making for research credit classifications, Internal Revenue Code Sections 199 and 263A, fixed assets, and transfer pricing

• Quickly predict the top permanent items and rate drivers affecting the effective tax rate (ETR).

Tax Analytics is tied to a broader business movement. Currently, for many companies, one of the biggest investment areas is ‘big data’ and predictive ‘analytics.’ According to PwC’s Global Data and Analytics Survey 2016, 39% of companies

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already are highly-data driven, and most of them already are applying analytics to other areas within the enterprise, such as supply chain, financial reporting, and marketing.

However, Tax functions generally have been slow to implement analytics and share in the benefits realized by other functional areas of the organization. Now is an opportune time for Tax functions to expand their capabilities by leveraging and aligning with the next generation of enterprise analytical tools and approaches.

Tax Analytic capabilities

Five levels of Tax Analytics

Tax Analytics Level 1


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Leveraging cross-functional systems

The Tax function often is one of the heaviest users of data within an organization—and the amount of data needed to support Tax continues to increase. With new capabilities available in enterprise resource planning (ERP), cloud and recent standard enterprise data warehouse solutions from the various enterprise and tax software vendors, Tax functions now can have access to more detailed information than before.

The tools also can allow Tax functions to share this information with other

Finance and planning groups in the enterprise—thereby reducing the need to replicate and download data solely for tax purposes.

Evolution of tools and approaches

Analytical tools have evolved quickly over the last decade and now are more user friendly and cost effective to implement. As a result, Tax professionals with minimal information technology (IT) expertise now can use these tools. Many Tax practitioners are adopting BI self-service tools–often starting with the areas of data visualizations and tax dashboards.

Higher expectations

The enterprise often expects Tax to contribute a higher level of analysis to the broader business forecasting, planning, and execution functions. The Tax function must meet these expectations while also addressing a growing number of external challenges, such as demands for global transparency, country-by-country (CbC) reporting

mandates, reputational/brand concerns, and an increasing number of audits and controversies worldwide.

No longer does Tax need to spend most of its time manipulating, calculating, and analyzing data in multi-threaded spreadsheet models, to rely solely on single-year historical consolidated trial balance level information, or to depend on primarily manual efforts. Tax must be instrumental in defining the governance, developing the solutions, and integrating cross functional data into its operations. It needs tax-ready data augmented with powerful visualization and predictive analytics tools to drive more efficient compliance processes and necessary data management, reporting, and analysis.

Tax must move away from a reactive, hindsight approach and shift to getting insight from data to drive significantly more enterprise value. This thought leadership explains the five levels of Tax Analytics, the capabilities of each level, and how Tax can start this journey.

According to a 2016 study conducted jointly by PwC and the Manufacturers Alliance for Productivity and Innovation (MAPI study), Tax functions spend less than 30% of their time analyzing tax data. Only 15% of them frequently utilize EPM and BI tools and almost 60% never use them.





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Analytic capabilities in Tax functions now…The five levels of Tax Analytics are shown in the adjacent diagram. The first three levels traditionally have been used to assist in the preparation of tax provisions and compliance. Note that they have historically required the Tax professional to know both the input and output, which may not be the case in the future.

Unfortunately, many organizations continue to use rudimentary forecasting techniques to perform common predictive tasks such as forecasting the ETR or estimating tax payments based on older historical apportionment data.

Tax functions should pursue the adoption of more sophisticated tools and techniques to move beyond just basic spreadsheet scenario planning to more ad-hoc reporting and visualization without having to perform data downloads to spreadsheets. Furthermore, internal and external stakeholder transparency requirements increasingly are demanding that Tax display far greater agility and alignment to a ‘single version of the truth’ of both accounting principles (most

Let’s dig deeper: Tax Analytic capabilities

Tax Data & Analytics Services

Stra

tegi

c im

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ct

How many?How much?

Basic descriptives

How are we doing?

Ratio,segmentation

How do webuild our own?

Ad-hocreportingwithout

data downloads

How do wecompare?

Benchmarking,metrics,trends &

visualization

What is likelyto happen next?

Forward lookingdecision making

based ontrusted data,

trends &anticipated

changes(what-if)

What shouldwe be doing?

Tax-modeling& optimization

based onstructure & legal

frameworks,as well asautomated

decision making

What dowe know?

Data exploration,advanced

learning, patterndetection &

decisionsupport

Stage 0

Basic dataand

Spreadsheets

Stage 1a

Metrics andReporting

Stage 1b

BI Self-servicewithout

Spreadsheets

Stage 2

Dashboard,Cockpits

Scorecards &Visualization

Stage 3

PredictiveAnalytics &

Tax Planning

Stage 4

PrescriptiveAnalytics, Tax

Modeling &Automated

Decision Making

Stage 5

Adaptivelearning:

Data Mining,AI &

MachineLearning

Mastery

Excellence

Enhancing

Essential

Businessreadiness

Comfort in making decisions based on data Interpreting data to give new insights to inform decisions

Pre-empting issues & targeting specificbusiness issues

Supportingtechnologies

Core Tax Reporting Statistical/PredictiveEnhance Reportingusing non-Spreadsheettools

Visualization/KPIsand Peer data

Modeling, Optimization& Automation

Machine Learning, AI& Decision Science tools

Targetedoutcome

Reliable data Basic reportingAbility to set metrics,benchmark performance &use visualization

Insight into drivers ofcompliance, cost andfuture tax implications

Ability to anticipate and detect potential issues and takemitigating action in advance

Core data solutions foundation for data management and governance




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Let’s dig deeper: Tax Analytic capabilities

notably Generally Accepted Accounting Principles in the United States) and financial planning and analysis (FP&A) data for sharing outside the enterprise.

Meanwhile, senior management continues to ‘raise the bar’ for Tax to be more nimble in providing insights and foresights to support analysis outside of routine compliance and hindsight-focused processes. Tax authorities and regulators also increasingly are requiring Tax functions to anticipate the tax-related data needed for audits such as the Standard

Audit File for Tax regulations that apply in many European Union countries. There also is a push for pursuing more proactive relationships with these external stakeholders before audits commence.

Additionally, extraordinary events, changes in business, and external challenges—such as CbC reporting, Schedule M-3 in the United States, and many local country requirements— frequently require more in-depth analysis of data than previously was the case for compliance tasks.

Other non-Tax groups within an organization likely already are using cutting-edge analytical tools or Software as a Service based tools. For example, many data-savvy companies employ data scientists and PhD’s with a firm grasp of machine learning, decision automation, and statistical modeling to develop complex algorithms that solve logistics problems in supply chain, sales, marketing, and risk management.




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Let’s dig deeper: The five levels of Tax Analytics

Tax Analytics generally can be grouped into five different levels:

At each level, tools and techniques can be deployed to solve specific tax needs in areas such as transfer pricing, compliance, indirect tax, tax credits, and tax provisioning. These analytical tools also can provide solutions for proactive monitoring, planning, forecasting, and decision automation.

Each of these five analytical levels requires unique capabilities, people skills, and technology-enabled solutions, as well as proper management and governance. See the various levels (i.e., essential, enhanced, excellence, and mastery) as illustrated in the core data solutions foundation model on page 4.

LEVEL 1Tax Business Intelligence (BI)Self-Service

LEVEL 2Dashboards, Cockpits,Scorecards &Visualization

LEVEL 3Predictive Analytics& Tax Planning

LEVEL 4Prescriptive analytics,tax modeling & decision automation

LEVEL 5Adaptive Learning: Data Mining, AI & Machine Learning

An increasingly common use case for Tax Analytics is for the continuous monitoring of transactional data within ERPs. Analytics can be used to identify tax data quality issues or business process exceptions with daily updates. An essential requirement is to identify exceptions accurately - delivering the best balance of true positive vs false positive exceptions. Key business drivers for adopting such analytics include minimizing financial impact, reducing manual effort, and increasing comfort and security around tax governance, risk and compliance.




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Tax Analytics Level 1—BI self-service…can minimize reliance on manual spreadsheets

What capabilities are present?

The first level of Tax Analytics is BI self-service, which focuses on making Tax professionals more proficient in developing reports and visualizations. Although Excel and Excel Add-ins remain useful tools within Tax and can be utilized as data sources to get started, Level 1 often can be achieved without having to download data to Excel and perform manual data manipulation.

Tax functions can benefit from BI self-service by adopting a combination of tools, training, and support to enable Tax professionals to perform ad-hoc reporting, create visualization, and conduct analysis, without using spreadsheets. BI self-service includes the deployment of BI tools by Tax staff to access centralized data in a simplified way that masks any database complexity. This alleviates some of the data problems associated with spreadsheet data replication and large volumes of data in areas such as sales and transfer pricing.

Tax functions can benefit significantly from rolling out BI self-service and reducing their reliance on traditional, hard-to-maintain Excel reports and multi-sheet workbooks.

Tax BISelf Service

Business Case

Less data replicationin spreadsheets

(Fewer errors)

Standardized repository of reports that can beshared & reused

(Simplification)

Lower cost ownership& less data integrationeffort

(Cost reduction)

Ability to quickly runad-hoc complexreports without ITsupport

(Agility)

Integrated calculations, filtering, lookups & cleansing

(Data integrity)

Centralized reportingrepositories

(Informationsecurity)




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Are off-the-shelf tools available from vendors?

Tax may be able to leverage a number of off-the-shelf tools available within the organization. Many vendors offer BI self-service ad-hoc reporting tools that can be used by Tax. Some vendors also blend their visualization and query tools, so there is a large overlap in capabilities of both dashboard and ad-hoc query tools.

What does implementation entail?

The first step of implementing a BI self-service rollout involves installing reporting tools, setting up security, and connecting to data sources. The second step involves Tax professionals learning how to use the tools on a trial basis to visualize data from Excel workpapers, standard tax return files, and other database sources.

Formal training, which can be added later, normally is a two-step approach involving a traditional one or two days of tool training, followed by a two or three-day hands-on training workshop. In this workshop, Tax personnel can start developing their own reports using their own data. Note that for very large or geographically distributed Tax functions, a BI self-service rollout may require longer deployment timelines for tasks such as tool installation, training, workshops, and user support.

An example of Level 1 of Tax Analytics is extracting standard financial data from a company’s compliance tool and viewing it through a data visualization tool instead of looking at standard list reports generated by the application.

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Four types of Tax Analytics Level 2 capabilities:

• Dashboards display a variety of data points that summarize data to KPIs and measures in a way that simplify the analysis of large volumes of data.

• Cockpits have a smaller focus and often are used to explore single subject areas, such as reconciliation, process steps, and targeted analysis.

• Score cards benchmark an organization’s data against prior periods, other internal legal entitles, industry KPIs, and other organizations. A balanced scorecard also can be created by assigning weights to each KPI to create overall performance scores.

• Data visualizations overlap with dashboards, but generally are tools that focus on more complex graphing, storyboarding, maps, and more advanced image generation. Most large software vendors offer both a dashboard tool and a more advanced data visualization tool.

Tax Analytics Level 2 – Dashboards, cockpits, scorecards, and visualizations…can enable real-time, more efficient decision-making


The second level of Tax Analytics leverages the abstraction of data into more meaningful information in either graphical form or tax-focused key performance indicators (KPIs) for benchmarking and comparisons. As data volume grows and organizations become more complex in both operational scope and geography, it is increasingly harder for Tax functions to conduct meaningful tax analysis in spreadsheets. Data visualization tools can help provide the meaningful tax analysis required in this more complex environment.


Today, there are various vendors and tools that can be leveraged. Many dashboard tools have reached a level of maturity and simplification that makes deployment easier than before.


The most difficult part of the dashboard implementation is not the development effort—often, it is the training and change management required to assure internalization and dashboard usage. Also, more complex scorecard and benchmarking dashboard projects often first will require defining performance measures that are relevant to the organization’s operation and scope, and that also are consistent with the organization’s overall Tax and business strategy. Therefore, for these types of projects, Tax functions should plan to spend significant time defining KPIs and selecting appropriate industry, geographical, process, and organizational benchmarks to be used in the implementation before dashboards displaying the performance measures are developed.

As a starting point, the increased agility and flexibility of Level 2 tools also make it possible to train and deploy dashboard and visualization tools for Tax in a matter of weeks.Tax Analytic capabilities



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When building out solutions at this level, it is important to take an iterative approach, considering various options. The Tax function periodically should review and experience the various dashboards to determine what outputs and solutions work best.

Tip

Dashboard example of cash taxes paid and total revenue by jurisdiction

Dashboard example of tax deductions by entity




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Tax Analytics Level 3 – Predictive analytics and planning…formulate a better understanding of the future


The third level of Tax Analytics focuses on ‘what-if’ analysis, planning, and forecasting. These can be used to determine impacts of regulatory changes, new business models, new operations, investments, and corporate life events (mergers, acquisitions, and dispositions).

Predictive analytics applies statistical methods using historical data and new information to forecast future tax obligations, credits, and financial impacts to the organization. Methods range from basic techniques, such as rolling forecasts and regression, to more advanced statistical methods that can account for trends, growth rates, and cyclical changes.

Tax functions that currently leverage Level 3 capabilities can standardize planning processes, provide improved insight into future tax allocation, and support the Finance team with inputs to yield better financial planning and improved cash flow forecasting. These capabilities allow the Tax function to look quickly into the future, as opposed to looking just at historical information.

Predictive analytics can include planning-focused activities that take into account regulatory and legal requirements for withholdings, allocations, payments, geography, transfer pricing, and periodic tax requirements that otherwise may go uncovered by statistical baseline forecasting alone..

Tip

Predictive Analytics &Tax PlanningBusiness Case

Less data replicationin spreadsheets

(Fewer errors)

Standardized repository of planning templates that is shared & reused

(Simplification)

Improved Insightinto Future CashFlow

(Cost reduction)

Centralized Tax Planning andAdvanced tools

(Standardization)

Improved Determinationof Future Levels of Allocations

(Cost Reduction)

Improved Insightinto Future Financialperformance

(ImprovedAccuracy)





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Many tools, methods, and technologies are available for predictive analytics and planning. Some vendors have built predictive analytics into their financial consolidation, BI, and FP&A platforms, often based on their ERP solutions, while other products are bolt-on statistical tools that provide specialized advanced functions.


Projects to execute predictive analytics and planning capabilities frequently focus on specific subject areas, such as corporate tax, direct tax, apportionment, trading partner, value added tax, sales tax, or other indirect taxes. These projects should be staffed with team members who are knowledgeable in each of these functional areas or with tax planning specialists who can develop planning templates. A ‘tax data scientist’ also may need to be engaged on very complex initiatives.

Organizations should look first to their enterprise applications (e.g., ERP, EPM, BI, Modeling, and Statistical) to leverage in deploying predictive analytic capabilities.

Tip

Leading Tax functions now may employ predictive analytics, forecasting, and planning as part of their regular day-to-day tax operations, rather than sporadically as time allows

Example of global international transactions





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Tax Analytics Level 4 – Prescriptive analytics, modeling, and decision automation…learning what to do


The fourth level of Tax Analytics focuses on using modeling and statistical methods to determine what should or may be done from a business, finance, operations, or tax perspective.

Prescriptive analytics is the application of tools that suggests to the user what should be done rather than what has been done. It uses statistics and algorithms to determine what an organization should do in areas such as asset management, legal entities, trading partner, geographical locations, investments, apportionment, operational transfer pricing, and execution of tax strategy.

Techniques applied in Level 4 also can extend to automate repetitive tax decisions, such as in a shared services environment, allowing the organization to focus on more strategic activities.

Decision automation often is referred to as ‘robotics,’ and can be deployed in areas such as research tax credit classifications, state apportionment, tax treatment of travel expenses, like-kind exchange matching, tax depreciation classifications, balance sheet and income statement driven Schedule M-3, and many other repetitive and rule-based tasks currently performed by tax personnel.

Tax andOperational

DataTax Decision orRecommendation

Deployed TaxAction toOperationalSystems

Human InterventionAnalytics

Diagnostic“Why did it happen”

Descriptive“What happened”

Prescriptive Tax Analytics“What is the optimal action”

Predictive Tax Analytics“How likely is it to happen”

Tax Modeling & Decision Support

Tax Decision Automation“Tax Process Robotics”

At this level, the platform could provide just decision support and possible recommendations, or be more advanced and execute these decisions without human intervention (e.g., robotics). [See diagram below.] Most organizations choose to start with decision recommendations at first and later, once the decisions and processes are confirmed by tax staff, begin to automate them.

Prescriptive analysis and human intervention





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Today, prescriptive tax analytics can be used in many areas, such as extracting the appropriate meals and entertainment data that is subject to the 50% limitation in Section 274(n) of the Internal Revenue Code and automating the limitation calculation without human intervention. Such analytics also may be used to calculate Schedule M-1s (e.g., depreciation) that normally would require a separate spreadsheet calculation, which is prone to errors, or may be used to evaluate whether foreign taxes paid are eligible for a foreign tax credit in the host jurisdiction, or to classify expenses for other credits, such as the research credit.

Benefits of Tax Analytics at Level 4 may include enhanced accuracy for compliance, less reputational risks, lower tax burden, and improved insight for the impacts of future investments. This level can enable the Tax function to review current functionality and benchmark itself to leading organizations within legal and regulatory frameworks. Level 4 Tax Analytics also may provide the ability to communicate models and outcomes to C-suite stakeholders in a more business-friendly way.

The benefits of decision automation or ‘robotics,’ in particular, may include a higher accuracy of standardized tasks, faster processing of tax data, and lower costs to perform standard tax operations with improved accuracy.


There are numerous tools on the market that can be programmed for Tax; however, few standardized tools offer advanced modeling and optimization

for the tax domain without significant customization to each organization’s unique situation.


Implementation of prescriptive analytics should be tailored to each organization since the objective of each initiative is likely to be different. Implementation also should include a combination of highly skilled Finance staff, Tax specialists, and Tax data scientists.

Tax functions should include a significant amount of ‘contingency time’ in the implementation plan since models often require significant alteration after being tested against real operational tax data. To reduce uncertainty, early data exploration should be included in the plan to detect any data quality or format issues.

Tip

Tax likely will follow the ‘big data’ trends of other functions within the organization. In 2016, almost a fifth of organizations surveyed reported that they are highly data driven and leverage prescriptive analytics as part of their data strategy. This change from descriptive analytics to prescriptive analytics is expected to increase significantly over the next few years as Tax functions become more sophisticated in analytics.





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Tax Analytics Level 5 – Adaptive Learning: Data mining, AI, and machine learning…enabling the Future of Tax tasks


The fifth level of Tax Analytics focuses on very advanced decision science techniques to discover trends, patterns, and errors and to support complex decisions using machine learning, AI, and data mining. These capabilities can be used to quickly review millions of pages of documents from public data sources, tax regulations, regulatory hearings, and tax rulings, as well as from non-traditional formats, such as voice and images, and then process and interpret them in context to a wider audience.

Machine learning – Machine learning involves computers acting without being explicitly programmed. There are many different approaches to building machine learning systems, but generally the purpose is to create system-generated recommendations, and answers, that are more efficient and more accurate than those from Tax personnel.

Machine learning requires significant transactional data to train the system to learn from outcomes and associated variables.

Machine learning capabilities have evolved significantly over the last few years. For example, on smart devices, text suggestions often use basic machine learning to predict what is likely to be typed. Voice recognition software also leverages machine learning to recognize specific voices. In the past decade, machine learning has facilitated self-driving cars, practical speech recognition, and effective web search. Machine learning is so pervasive today that most people use it dozens of times a day without realizing it.

Imagine the potential to…

• Read millions of pages of information in seconds (e.g., all Treasury regulations, 10 years of unstructured tax workpapers, and 10 years of financial statements (including footnotes)) and assess the tax risk of an organization or predict the top three items affecting the ETR by jurisdiction…all within seconds

• Process and classify millions of global transaction records from multiple source financial systems and automate the tax classification work that currently takes thousands of hours to do manually (including fixed assets, operational expenditures, revenue, and indirect taxes)

• Classify tax filings flagged for audit with over 95% accuracy based on as little as 19 attributes and key figures

• Explore proprietary data sets and unstructured data (e.g., documents, emails, text messages, contracts, and legal language) for monitoring Tax operations and rapid access to internal resources.

Unlike the vast majority of Tax functions, tax authorities are using machine learning. For example, the Internal Revenue Service reportedly has experimented with machine learning systems to flag tax returns for audits.





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The conceptual model below is an example of the extraction of source data with tax rules and other constraints applied to automate the classification and calculation of tax adjustments and data. This model is already being used in other functional areas within organizations and is being piloted with different tax scenarios.


An experienced data scientist with an advanced degree in statistics, computer

science, or decision science is required to deploy machine learning in a Tax function. For most organizations, several classification schemes and multiple rounds of learning processes must be executed before the final approach is selected and implemented. Model decisions subsequently must be monitored to see how the learning algorithm improves over time.

Data and text mining – Unlike the other levels of Tax Analytics, data mining is the process of finding previously

unknown relationships and patterns in large data sets. It often is used for tax data exploration, fraud detection, event grouping, flagging of unusual tax events, theory building of transactional tax impacts, and operational monitoring. This task normally is performed by data scientists.

AI – AI leverages advanced statistical or computer science tools to mimic human decision-making. The field has advanced dramatically over the last few years and has the potential to replace many of the routine human decision processes of Tax professionals. As a result, it can free up human capital to focus on special events, audits, and unstructured ad-hoc decisions.

Exploratory data analysis (EDA) – Many AI and machine learning projects start with a shorter-term EDA initiative. These initiatives explore a client’s available data for patterns, tax decision support, tax decision automation, relationships, groups, segmentation, KPIs, tax issues detection, data quality, and descriptive tax analysis.

COUNTRY 1LE 1

T&E:Tx 11242Tx 45590

Charitable Don:Tx 31355Tx 31245Tx 88099

COUNTRY 2LE 1

Marketing:Tx 11642Tx 46590

Fines and Pen:Tx 21355Tx 11245Tx 58099

COUNTRY 3LE 2

Capex:Tx 11442Tx 46990

Fixed Assets:Tx 21345Tx 11235Tx 58089

COUNTRY 4LE 3

Provisions:Tx 11446Tx 46770

Accruals:Tx 11345Tx 21235Tx 78089

Tax Classification Summary Example

General ledger accounts

Trial balances

Tax rules

ConstraintsTA

X

MACHINE LEARNING





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Tax Analytics can facilitate the road to higher levels of capabilityTechnological breakthroughs continue to facilitate access to information. The ability to gather and analyze data in real time is not merely a competitive advantage, but also a requirement across all aspects of a company’s business–from marketing to its supply chain to the Tax function.

Integration of Tax Analytics can help companies achieve greater functionality. The primary challenge to incorporating data analytics into Tax is a general lag in the digitization of tax data. Unlike most corporate financial data that is found in companies’ ERP systems, tax information continues to exist in Excel files and documents emailed back and forth between Tax functions and tax service providers in an ad-hoc approach. This lack of access to digitally structured data hinders the implementation of data analytics for Tax.

New technologies and techniques to analyze data are evolving quickly,

enabling processing of both structured and unstructured data and assisting Tax in managing and processing data in ways not previously imagined.

How to get started…

Developing a roadmap for the future

As part of a Tax function’s overall tax technology roadmap, Tax Analytics should be considered as one of the core capabilities and solutions, not dissimilar to data management, provision, or compliance. Tax functions should have a clear vision for what tools, approaches, and investments are needed for the future.

Assessing how your company is leveraging analytics

Developing a Tax Analytics plan should include an evaluation of how the rest of the organization is currently leveraging the power of data analytics and identifying tools and techniques that can be imitated. It also will be important to identify which data sources can be leveraged for Tax.

The Tax function also should evaluate how it currently uses the five levels of Tax Analytics described above. Is the Tax function performing within any of the levels? Which areas within its operation need improvement? Which value-added, analytical tasks could become more efficient from automation? What return on investment does the C-Suite demand from the execution of tax planning?

Tax functions no longer can ignore the capabilities and benefits that Tax Analytics can bring. Now is the time for the Tax function to develop or incorporate analytics into its overall tax technology roadmap in order to share in the benefits already being reaped by other functions within the organization. Resulting capabilities should help Tax exceed stakeholder expectations for years to come.





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