smarter compliance - ey - usfile/ey-smarter-compliance.pdf · 2018-10-18 · smarter compliance...

Smarter complianceStarting out with artificial intelligence in financial services tax

“ There is an acceptance that AI will change everything in 10 years’ time, but little appreciation of how it could, and should, impact businesses right now.”

C h ris M az z ei EY Global Chief Data & Analytics Officer

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C ontentsWhen the financial services industry works well, it creates growth, prosperity and peace of mind for hundreds of millions of people. No other industry touches so many lives or shapes so many futures.

At EY Financial Services, we share a single focus — to build a better financial services industry, not just for now, but for the future.

We train and nurture our inclusive teams to develop minds that can transform, shape and innovate financial services. Our professionals come together from different backgrounds and walks of life to apply their skills and insights to ask better questions. It’s these better questions that lead to better answers, benefitingour clients, their clients and the wider community. Our minds are made for protecting financial services. It’s how we play our part in building a better working world.

ey.com/FSminds

1 https://betterworkingworld.ey.com/growth/growth-barometer-2018 2 https://www.ey.com/gl/en/issues/business-environment/ey-innovation-matters-putting-artificial-intelligence-to-work/

Smarter compliance Starting out with artificial intelligence in financial services tax

“ The real value in AI is the combination of deep tax expertise with innovation to produce a result that neither can achieve alone.”

Richard Clough lo al hie ata ficer ax

rnst oung

The value of artificial intelligence to tax

From driverless cars, home assistants and drones to surveillance and data, artificial intelligence (AI) is a common theme both in the media and increasingly in our day to day lives.

Most organizations are only just starting to explore the value that AI can bring to their business. This guide looks at the technologies available and contains illustrations of potential use cases for tax functions, and how they can be deployed to add immediate value to the business.

In 2017, 74% of companies surveyed by EY across the globe said that they did not expect to ever adopt AI technology. Just one year later, 73% of companies said they expected to deploy AI and other advanced technologies in their business in the next two years1. However, only 6% of companies have already adopted AI.

There is significant hype around the possibilities of AI, ranging from the revolutionary to the disastrous.

For tax functions, the answer is likely to be more mundane — AI will change how we work and what we can achieve, but there is a need for expertise and care in its deployment.

Using artificial intelligence in tax functionsCompliance challenges represent the most fertile ground for deploying AI, particularly where large volumes of data are involved.

Employment, indirect and customer tax frequently have a range of challenges where AI can be immediately deployed to improve compliance, assure reporting or reduce workload.

Similarly, the end-to-end reporting process has a range of opportunities for adoption of AI — from tools which can classify transactions, or determine whether legal and professional expenses can be expensed for tax purposes, to a combination of tools which can improve the year end reporting cycle.

Regulatory change and remediation issues can also be streamlined by introducing AI processes, for example to preclassify cases prior to manual reviews.

7 3 %of companies have either

adopted AI or expect to in the next two years

or a wi er view o the environ ent see utting artificial intelligence to wor on lo al nnovation age


What is artificial intelligence?

Artificial intelligence encompasses a wide spectrum of technologies and solutions; there is no single agreed definition of what is and isn’t covered under the AI umbrella.

Many of the mathematical and logical principles underpinning artificial intelligence have been studied for decades. But we are now at an inflection point where data and technology have evolved to allow these tools to be deployed in real world scenarios in a practical and sustainable way.

Some technologies have a more immediate application in tax than others. Many of the examples in this document are underpinned by machine learning techniques, which have the particular benefit that they can be quickly prototyped.

More strategic solutions will combine elements of AI with other advanced technologies (see next page) — these are the solutions which can deliver the most value but require greater commitment and expertise in their development.

Key termsMachine learning is a rapid growth area for AI technology. Machine learning draws from a wide range of algorithms to analyze data, and identify anomalies, make decisions or flag risks. It can broadly be broken down into two categories; unsupervised which requires no human annotations to train, and supervised which analyses labelled data and extrapolates to new scenarios.

A key benefit of Machine Learning is that as rules are not hard-coded, Machine learning tools can learn and adapt with new data or changing rule sets. Machine learning has a wide application in tax, which can include reviewing large data sets, decision-making and assurance.

Simple Machine learning tools can be rapidly deployed to tackle current problems, or combined with other tools to produce intelligence-led solutions to tax problems.

Deep Learning combines a range of machine learning tools to replicate decision-making processes and can achieve far more than a simple machine learning process with less human intervention.

Deep learning tools can be incredibly powerful tools — and would for example be found in autonomous cars — but require far more investment and expertise to develop.

Natural Language Processing (NLP) covers tools which analyze and even copy natural language and speech.

They may be used to extract useful information from emails or phone calls, to sweep the internet for information, or to interact with customers as part of a chat bot.

Advanced applications which combine NLP with document scanning technologies (commonly referred to as document intelligence) can read contracts and other documents to look for key clauses, provide complete search capabilities across paper documents or extract structured information from documentation to support other processes.


Robotics and process automation are often kept separate from other AI technologies. Simple robots may display no intelligence whatsoever, but can automate processes which require repeated steps to complete a process.

Robotic process automation is particularly useful for processes that are data intensive, repetitive and rule-driven. Where existing processes involved either manual calculation or result in high error rates, robotics can deliver significant enhancements to the process.

When combined with even basic artificial intelligence, robotics can accomplish an ever increasing range of tasks, freeing up people to work on higher value add opportunities.

Like robotics, Analytics may be considered separately from AI and again — simple analytics may contain limited intelligence but still be very powerful.

Analytics is useful for identifying patterns across a data set, for improving understanding of the business and to support in the generation of reporting.

When combined with machine learning in particular, analytics can be a powerful assurance tool, or an important part of a line of defense strategy.

The umbrella term data covers a wide range of skill sets and opportunities. This includes data visualization which can assist with interpreting data to support decision-making, and data extraction and exploration which are critical components of automated and AI solutions.

But, machine learning and analytics solutions aren’t just reliant on data, they can also help organizations understand where their data is lacking, and can even be deployed to fix recurring data issues, source new data from third parties or to resolve conflicting data extracted from different systems.

Open APIs are not themselves part of AI but provide an important tool for accessing and enhancing data. Open APIs allow for access to third party systems on an as needed basis and can be used to access data from third parties — such as address look ups, or credit reference agencies. APIs are increasingly used to send data to tax authorities in place of traditional return processes.

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Optical Character Recognition

Robotic process automation

Intelligent automation

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Natural Language Generation

Natural Language Processing

Machine learning

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Data analysis

Data visualization

ETL and data exploration


The following pages set out common AI tools, and possible use cases within tax; including ways in which EY is already deploying AI with clients to address tax problems. These examples primarily deploy machine learning techniques to address common tax issues. Machine learning is particularly useful for tax, because it is good at tasks which involve:

► Classifying data, for example, determining reportable customers or whether an expense is deductible

► Detecting anomalies, for example, misbooked transactions which need further investigation

► Predicting outcomes, for example, future effective tax rates based on existing data

► Identifying previously unknown patterns in data

The case studies are deliberately simple to illustrate the potential applications of the machine learning tools, but can be expanded to a high degree of complexity.

Using artificial intelligence


Introduction to machine learning

Detecting anomaliesAnomaly detection is a form of unsupervised learning, which takes a large data set and searches for outliers and anomalous data which differs significantly from the rest of the data set. Algorithms will report back not just which entries are anomalous, but provide scoring on the extent to which they lay outside the rest of the data set.

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ata set an a there ore e incorrectl oo e an nee urther investigation

ClassificationA computer is supplied with training data, such as a set of transactional data with the classification decisions already made — for example identifying whether an expense is deductible or not. An algorithm then “learns” what makes these entries unique and uses this logic to analyze future transactions or activity.

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to eter ine which alances are eligi le or ca ital allowances at nor al or enhance rates inclu ing s en ing on ro essional services ees

Identifying patternsClustering algorithms seek to group data points together to identify entries with common features. Unlike a more traditional two dimensional scatter graph, clustering algorithms work well with more than two variables and can also identify which variables give rise to the most anomalies in your data.

xa le lustering is a use ul tool or loo ing at custo er ata to i enti atterns which a in icate un nown oc ets o clients or exa le high alances in retail an ing increase tax ris s or hel to strati clients or tax ris s

Predicting outcomesRegression algorithms allow new values to be predicted from existing data by identifying the ‘line of best fit’ that explains current data. Like clustering, they can work with multiple variables and can be calibrated to give not just a predicted value but also confidence levels of given outcomes.

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arising ro changes to the tax re uire ents in countries


Convergence: the power of combining tools

Individual AI tools can provide significant value when applied to specific challenges. But the benefits of AI are most apparent when multiple AI technologies are brought together to provide a holistic solution to a wider business issue. The example below looks at the deployment of AI technologies including document intelligence, machine

learning and natural language processing along with robotics across a normal compliance process.

Critically, each step is modular — it can deployed separately and value is added with each addition whilst freeing up subject matter experts to focus on higher value tasks.

Example: tax compliance process

Existing data is supplemented with data extracted directly from underlying documents, such as invoices or contracts.

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etection

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► Robots can automate existing processes without changing them

► Current personnel can be freed up to perform more complex work

► Once automated, process change, improvements and artificial intelligence can be considered

► Robots and humans work together, not in isolation

eview

Continuous knowledge maintenance through a combination of robotics and AI which pulls updates from external tax sources.

Combined analytics and machine learning tools to review data, identify outliers and escalate issues with suggested corrections.

Machine learning led review of data for example to automatically assign cost categories from non-tax sensitised ledger data.

Use of natural language tools to allow human reviewers to interrogate the data, or to allow response to tax authority queries.


Convergence: Case Study

EY’s Customer Tax AI & Analytics for data quality, classification and risk detection can assist you to automate and improve your tax operations:

► Data quality remediation provides automation and accuracy

► Improved classification accuracy to improve customer experience and reduce cost of operations

► Risk anomaly detection and predictive controls to better manage inquiries and controversy

► 20%–50% cost reduction in Customer Tax operations through introduction of AI enabled process

Example: FATCA and CRS end-to-end process

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nal tics an ano al

etection

ata ualit achine

learning

eview an attestation

lassification

Su ission

Intelligent cognitive tax form validation and machine learning to review customer due diligence, learning from past behavior.

Data quality remediation, interrogating third party data sources with external APIs to enrich data sets.

Data quality remediation and cleansing to increase accuracy and confidence for onward processes.

Classification machine learning to learn the logic and patterns to accurately identify the reportable population.

Intelligent reporting, AI rule based logic, and machine learning for risk anomaly detection and remediation.

Machine learning can enhance the understanding of why regulators flag certain submissions based on NLP and internal/external data.

ata enhance ent


Jargon buster

APIs (application programming interfaces)APIs are how different systems talk to one another and exchange information.

Cloud Cloud based solutions are hosted by a third party, in their own data centers rather than within the IT infrastructure of the organization. Cloud computing offers access to latest technologies and flexibility to needs.

Deep LearningA form of AI which combines a range of machine learning tools to replicate decision-making processes and can achieve far more than a simple machine learning process with less human intervention.

ETLExtract, Transform & Load — a commonly used term for the process of finding data, manipulating it into the right format and uploading it into the tool to allow the system to analyse or use it. This is often a significant part of any project.

Machine learningA form of AI which draws from a wide range of algorithms to analyze data, and identify anomalies, make decisions or flag risks. It can broadly be broken down into three categories; unsupervised, supervised and reinforcement learning.

Natural Language ProcessingCovers tools which analyse and even copy natural language and speech. They may be used to extract data from emails or phone calls, or to interact with customers as part of a chat bot.

Open SourceOpen source software is available to everyone, including the source code on which it relies. Open Source solutions are frequently easier to deploy quickly as they do not require licensing or commercial arrangements. Python is an open source language which supports machine learning.

Trust in AIAn approach to developing AI solutions which embeds trust from the early stages and includes transparency, explainability, resilience and performance.

Read more here: https://www.ey.com/en_gl/digital/how-do-you-teach-ai-the-value-of-trust

Reinforcement learningA form of machine learning in which a system is ‘rewarded’ based on the decisions it makes and learns with a view to maximize those rewards. The algorithms are common in learning AIs (for example those which learn to play games against human players) but can be used in any decision-making processes.

Supervised LearningA form of machine learning in which a system is given labelled information and learns how to apply that to other unlabelled information — for example a system that is given information on reportable vs. non-reportable customers and applies it to unclassified accounts.

Unsupervised learningA form of machine learning which takes unclassified information and looks for patterns, correlations and outliers.

VisualizationDescribes the need to present the output of AI and data to all stakeholders, recognising that different users may benefit from different outputs. Common tools include Tableau and Power BI.

XAI (Explainable AI)An emerging concept in AI, explainable AI refers to the ability to determine why a system made a decision, down to a case by case analysis if needed.


AI projects don’t have to be large scale programmes with long lead times.

Where data is already available and technology can be deployed, developing a proof of value solution can be done within 90 days — confirming that the problem can be addressed with AI, and even yielding immediate results.

The proof of value is kicked off by identifying existing problems and determining the use cases that will add the most value to the business, understanding the question and the expected value is key for delivering successful AI projects.

The proof of value should be thought of as a launchpad for further development, establishing the tools and data available, and the process for delivering AI-led solutions. Once the first phase is completed, the framework for further AI development can be used to develop a wide range of solutions.

Example: FATCA and CRS reports submitted to tax authorities in 2018 are already available in a readable format, which means that it can be assessed with machine learning tools to identify problems within the data reported, and to identify issues which can be addressed before 2019.

“ AI is likely to create winners and losers, and those who start adopting the technology early stand to be at a significant advantage.”

Nigel Duffy EY Global Innovation AI Leader

Using AI: the 90 day proof of value


Using AI: the 90 day proof of value

Model assurance

Data security

Platform

Data exploration

Model development

Delivery model

Fr

amew

ork

T ech nology

A nalys is

Technology underpins the deployment of analytics and AI solutions. The underlying technology can be hosted in your systems, either cloud-based supported by a third party, or a combination of the two.

► Data security: in all cases, it will be necessary to address the data security and data protection issues at the outset and the ability to move data may dictate other aspects of the project, including the technologies which can be deployed.

► Platform: determining the technologies is a key step in the early stages of an AI programme. Popular machine learning solutions include the open-source Python and R.

The platform will also need to include access to a database, and tools to visualize the outputs of the AI. They may also be combined with case management tools where further actions are required, for example in the case of remediation exercises or risk reviews.

1. Technology

The analysis phase is where the most value is added to the project. The available data is assessed alongside the identified problem, with a combined team working together to identify the best solution. Tax knowledge and experience is key at this stage to make sure that the solution solves the intended problem in a compliant way.

► Data exploration: data sets are rarely perfect. Initial analysis of the data set can identify problems which could stop the AI model from working, and steps can be taken to address shortfalls or enhance the data set.

► Model development: this includes building and configuring algorithms to support the solution, along with developing MI or visualizations of the data. This is likely to be an iterative process, developed in collaboration by the team.

2. Analysis 3. Framework

Establishing a framework for the programme means that it can be developed and enhanced, and that the inputs, process and outputs can be explained to management, tax authorities and regulators.

► Delivery model: establishing a robust, modular framework allows additional features to be added later building on the initial project, while also establishing a model which can be used with regulators to gain comfort on the development of the solution.

► Model assurance: it is critical that models developed for analysis are trusted both internally and externally — black box solutions are unlikely to be acceptable to tax authorities and regulators and will need to be fully explained.


What risks should businesses consider when looking to implement AI?

(An extract from “Putting Artificial Intelligence to work” on EY Global Innovation page)3

The biggest risk is non-adoption. Every challenge in the world, and in business in particular, is an opportunity for AI. Many early projects will have a low return on investment (ROI) and a limited impact — they primarily provide learning opportunities. But that learning is essential and the first step on a transformational journey that will touch every business.

Another big risk is talent. The AI community is still very small, as is its most important subfield, machine learning (ML). Good talent is hard to find and hire. This leads to a significant risk of the Dunning-Kruger effect — people

believing they know much more than they do — and the risk of over-promising and under-delivering is high.

Bias in Machine learning is potentially a problem — if there’s bias in your data, AI will amplify it unless you specifically put in checks to prevent this from happening. AI systems also make decisions faster, so businesses must develop appropriate risk monitoring and management approaches.

Finally, overregulation and regulators’ lack of understanding about these technologies could cause issues. It is essential that enterprises accelerate their learning and the development of internal controls so they can have informed, educated responses to regulators.

3 https://www.ey.com/gl/en/issues/business-environment/ey-innovation-matters-putting-artificial-intelligence-to-work


How will tax authorities respond?

The introduction of AI presents opportunities for organizations to improve their compliance, to change and streamline their approaches or to undertake new activities that were previously prohibitive.

The response of tax authorities to the use of these new approaches by taxpayers will depend on what is changing and how well the changes are communicated to them. Early engagement is likely to be key to significant changes, while an open and transparent approach will be important where the use of AI results in changes to compliance practices.

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Stages of tax authority adoption

Scepticism: tax authorities may initially be reluctant to accept new technologies in place of existing processes. Changes in methodologies could lead to queries from authorities, and any new approaches which are in favour of the taxpayer are likely to be contentious. Early engagement with tax authorities is key.

Recognizing opportunities: tax authorities should welcome improvements to compliance through data enhancement, machine learning and the use of the third party data. Better compliance will be welcomed — but establishing a clear audit trail and explainable AI will be critical — no black boxes.

Adapting: AI will provide opportunities to apply rules in a more effective way but not envisaged by the current rules. These opportunities to improve compliance will need significant engagement with tax authorities, perhaps at an industry level, to seek rules changes which allow AI to be deployed to operate.

Mandating: regulators are not currently requiring the use of AI or advanced analytics but in an evolving space, this will need to be monitored, particularly as tax authorities become more familiar with these technologies.


What next?

Starting AI programmes can take many forms, from problem-solving hackathons to assessing key tools and solutions across the market.

It is rare that any single person will understand the business needs, the tax rules and the data science. Instead there is a need for people who understand broader business goals and strategy, problem solvers who understand how to solve problems with AI, and technology specialists or data scientists who know how to provide a data and analytics project.

At EY, we focus on bringing together teams that leverage tax and AI expertise together to build, develop and oversee AI systems that have tax knowledge embedded from the start and are designed to provide trust and confidence in the output.

To discuss how EY can help you use AI to support your tax strategy, or to change the way you deliver today, please get in touch with your normal EY team, or contact one of our dedicated Financial Services AI team.

Richard Clough lo al hie ata ficer ax

rnst oung Email: [email protected]

United Kingdom

David Wren ssociate artner rnst oung

Email: [email protected]

David Hush Senior anager rnst oung Email: [email protected]

Asia-Pacific

Kelum Kumarasinghe artner rnst oung


Americas

Anne Farrar artner rnst oung


EY | Assurance | Tax | Transactions | Advisory

About EYEY is a global leader in assurance, tax, transaction and advisory services. The insights and quality services we deliver help build trust and confidence in the capital markets and in economies the world over. We develop outstanding leaders who team to deliver on our promises to all of our stakeholders. In so doing, we play a critical role in building a better working world for our people, for our clients and for our communities.

EY refers to the global organization, and may refer to one or more, of the member firms of Ernst & Young Global Limited, each of which is a separate legal entity. Ernst & Young Global Limited, a UK company limited by guarantee, does not provide services to clients. For more information about our organization, please visit ey.com.

© 2018 EYGM Limited. All Rights Reserved.

EYG No. 011614-18Gbl

EY-000069782.indd (UK) 10/18. Artwork by Creative Services Group London.

ED None

In line with EY’s commitment to minimize its impact on the environment, this document has been printed on paper with a high recycled content.

The tools and algorithms included in this document are intended to illustrate the potential use cases for machine learning and artificial intelligence in a tax environment. Actual deployment of these tools and algorithms will depend on technical feasibility and the time to deploy the underlying technology may vary. Before being implemented, the proposed use cases in this document require detailed consideration of the relevant data protection provisions.

This material has been prepared for general informational purposes only and is not intended to be relied upon as accounting, tax or other professional advice. Please refer to your advisors for specific advice.

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smarter compliance - ey - usfile/ey-smarter-compliance.pdf · 2018-10-18 · smarter compliance...

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