A new world: how big data could transform Transfer PricingTransfer Pricing Special EventSeptember 22, 2016
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Session SpeakersShamus RaePartner, Central Solutions, KPMG in the UKKomal DhallPartner, Global Transfer Pricing Services, KPMG in the UKKirsty RockallDirector, Global Transfer Pricing Services, KPMG in the UK
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Agenda Topics• Mapping the Journey• The Path to Cognitive Automation• Transfer Pricing and Big Data Today• The Art of the Possible
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Thinking Forward
KAI
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Mapping the Journey
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Application of exponential perspective to the future
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The Path to Cognitive Automation
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Information technology is at another major inflection point: the digital revolution
“Tell me what to do”
Interactive learning/hypothesis generation
Organizations are experiencing a profound change due to the convergence of technologies:
Generation 1:Tabulating era
Generation 2:Programmable era
1890 2015
Machine counting
Punchcards
Generation 3:Cognitive era
The world of big data: 2.5 billion gigabytes of data each day, 80percent
ofwhich is unstructured
We are here:that “momentof opportunity”
Inflectionpoints
1960
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The benefits of a digital labour forceWave 1: Labour arbitrage Wave 2: Labour automation
Source: The Outsourcing Institute, Three Secrets Your Traditional Service Providers Are Not Telling You, June 2014KPMG analysis
Social
Mobile
Cloud
Data/Analytics
CognitiveDigital
Business Models
Revenue/profit correlated to people Revenue/profit not correlated to people
15 – 30 percent Cost take out
40 – 75 percentCost take out for relevant functions
Model is scalable to the extent that you can scale labour
Model is scalable, and is largely independent of labour growth
Access to low cost labour necessaryto provide continuous value
Access to “rocket scientists” who can codify manual processes
Custom/complex, legacy: “Your Mess for Less” Transformative – new way of doing business
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The spectrum of digital labour
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Cognitive automation is evolving rapidly
Cognitive automation
— Artificial intelligence— Natural language recognition and processing— Self-learning (sometimes self optimizing)— Processing of super data sets— Predictive analytics/hypothesis generation— Evidence-based learning
— Built-in knowledge repository— Learning capabilities— Ability to work with unstructured data— Pattern recognition— Reading source data manuals— Natural language processing
— Macro-based applets— Screen level and OCR data collection— Workflow automation— Process mapping— Self executing
Basic process automation
Enhanced process automation
MachineLearning
Large-scaleprocessing
Adaptivealteration
Artificialintelligence
Big dataanalytics
Naturallanguageprocessing
Processing of unstructured data and
base knowledgeRules engine Visual Data
CollectionWork flow
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The path to cognitive automationStrong market players are rapidly emerging
ExampleIPSoft’s Amelia™ utilizes Artificial Intelligence and advanced semantic reasoning to rapidly understand questions, provide answers, and escalate to humans if needed.
Sample Providers— IPSoft— Arago— Automation Anywhere — TCS ignio— Wipro
ExampleFirms are leveraging IBM Watson’s™ sophisticated ontologies, natural language processing, machine learning, pattern recognition and probabilistic reasoning algorithms to aid skilled employees and robots with complex decisions. Other firms are in beta or early stages.
Sample Providers— IBM Watson— Cognitive Scale— Chatterbox
ExampleBlue Prism® has automated tier-1 inquiries (e.g., address change) for a US-based online bank.
Sample Providers— Automation Anywhere — Blue Prism— UiPath— Interactive Media
Rules Learning Reasoning
Transactional acceleration Intelligence augmentation to support right decisions faster
Basic process automation
Enhanced process automation
Cognitiveautomation
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Robotics and cognitive automation
Establishing an RPA roadmap Major financial services provider
Context/client challenge
— Completed several data center consolidation and virtualization projects, and was considering further optimization, including automation.
— Original focus was on traditional outsourcing, with hope that the service provider would achieve enhanced results with better economics.
— Lack of awareness of the potential efficiencies that can be gained through incorporation of RPA into their data center strategy.
Approach
KPMG provided:— Expertise in the RPA market and
offerings, and their applicability to the client’s environment, strategy and drivers.
— Development of a Request for Solution that asked the right service providers the right questions for the client’s specific situation.
— Facilitation of service provider responses and joint solutioning sessions with the client, helping to ensure that the client received the right information to execute and finalize its data center strategy.
Benefits
— Client now including RPA as part of its data center strategy.
— Dramatic reduction of human handling of incidents through use of thousands of “out-of-the-box” automated service solutions.
— Full integration with existing service management tools.
— Improved service levels with evidenced-based outcomes.
KPMG client case studies
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Robotics and cognitive automation
RPA transformation Major telecom provider
Context/client challenge
— Ineffective business processes required “swivel chair” access to multiple systems and caused high degrees of errors or re-work.
— Lack of process and tool implementation capabilities.
— Traditional IT teams were not delivering solutions to match demand
— Business units were beginning to look at transforming customer experience beyond basic automation – Virtual Chat, Decision Support etc.
Approach
KPMG assisted the client to:— Define the strategy for the RPA
program, creating a roadmap for increasing the program’s maturity.
— Execute the strategy and roadmap to manage the program.
Key activities in the approach included:— Establishment of an RPA Center of
Excellence to build a delivery and operating model focused on scaling implementations and enabling benefit realization/management.
— Identification of functional opportunities to leverage RPA solutions.
— Creation of a model to enable business units to build and implement RPA solutions to address their respective process pain points.
— Thought leadership on Cognitive automation and vendor landscape
— Development of use case selection criteria for cognitive automation
Benefits
— Deployed 200+ robots over the last 12 months
— Generated annualized savings of $15million+
— Pipeline of 300+ automation opportunities
— Trained over 400 business users on the tool
— Reduced errors and rework, and improved operations consistency.
— Improvements in overall employee and customer experience
— Cognitive Automation Value Assessment (CAVA) underway to identify potential usecases
KPMG client case studies
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Robotics and cognitive automation
Cognitive Automation strategy and roadmapLarge healthcare provider
Context/client challenge
— Client was interested in applying advanced cognitive technologies in a shared services environment; needed help in identifying opportunities and conducting a market study of available solutions.
— Client was piloting basic automation for a limited number of P2P, H2R, and R2R processes.
Approach
KPMG assisted the client to:— Conduct a detailed assessment to
identify candidates for cognitive automation; prioritized the opportunities based on value proposition, complexity of solution, and organization readiness.
— Conduct a market scan to develop a vendor/capability matrix for 100+ vendors and service providers and identify high potential cognitive automation solutions.
— Create an implementation roadmap to develop and sustain cognitive automation capabilities, including the supporting CoE org structure.
Benefits
Client evaluating KPMG’s recommendations, which are expected to deliver: — Significant reduction in existing and
future human labor needs.— Improved service levels and user
experience with evidenced-based outcomes.
— Integration with existing enterprise systems and basic automation technologies.
— Ability to expand into new markets and rapidly add new customers.
KPMG client case studies
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Transfer Pricing and Big Data Today
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Where we are todayReturn on investment
Complexity
Benchmarking platforms
ERP analytical solutions
Legislative update tools
Automated risk assessments
Data management
Document writers
Work flow tools
Raw public data platforms
CBCR software
Forensic data mining Big Data
Analysis?Cost allocation tools
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The Art of the Possible
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How do we get there
To prepare, you need to identify the content you will need in the next 4 years and consider:
How can I reduce the cognitive load on my business to utilise my
content?
What are the ethical and moral values that inform the content
produced?
What is the data required to produce the desired content?
Data Cognitive load Values
Content strategy
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DataWhat are the data sets for tax?
Structured Unstructured
Internal
External
Existing data used for transactional purposes is not the focus of today’s session
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Cognitive loadTransfer Pricing Framework
Pricing Methods
Comparability
Documentation
Deadlines
Disclosures
Audit Rigour
Double Tax Implications
Advanced Pricing Arrangements
Single Jurisdiction
Domestic Tax Legislation
Relevant Rules and Regulations
Judicial Interpretation
Local Industry
Company Information
Corporate Structure
Value Chain Analysis
Commercial Strategy and Stakeholders
Multiple Jurisdiction
Global Industry
IFRS
International Tax Landscape
Relevant Rules and Regulations (OECD)
Inputs
What is the framework for contextualizing the data?
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Where we could be Enriched analysis before we humans make the decisions
CognitiveBenchmarking
Report production
Value Chain Engine
Full cognitive analysis
Case law and legislation
tailored analysis
Working capital/ interest
deductibility calculator
Complexity
1 – 2 years 3 – 5 years
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Example - cognitive benchmarking
Intrinsic Germane
Inherent difficulty of the content that will enable the business to decide
and act
Framework for contextualising and processing data to produce content, including sources of data and their
relationships to each other
Extraneous
Complexity of the content required to provide the information for the
business to decide and act
Benchmarking strategy
Prior usage data and profit trends
Macro economic data
Company websites
Publically available financial data of peers
Industry data
Commercial regulatory data
Local tax rules and regulations
Tax authority: “why are your benchmarked comparables
appropriate for your intra-group transactions. I disagree with your
analysis”
Shortened list of most appropriate comparables
Validate with qualitative data
Feed back into the Germane feed as learnings
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Example – full cognitive analysis
Intrinsic Germane
Inherent difficulty of the content that will enable the business to decide
and act
Framework for contextualising and processing data to produce content, including sources of data and their
relationships to each other
Extraneous
Complexity of the content required to provide the information for the
business to decide and act
Location of CEO
Current Transfer Pricing policy
Categorisation of decisions made by the CEO
Value of decisions made by the CEO
Future strategy of the business
Industry outlook
Effective Tax Rates
Profit profile of the business
Local tax rules and regulations
What would the impact be if I moved my CEO from the UK to
Germany?
Considerations
Tax authority challenge
ETR
TP adjustments
Commercial impact
Outcome
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Points To Leave WithIs your tech strategy for tax taking into account:• The drive for efficiencies• The commercial strategies you may need to support in the future and
therefore enriched and evidenced analysis• Defence against technologically advanced tax authorities• The need to simplify your key messages with facts
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