An exclusive presentation by Mr. Imam Hoque, General Manager, Advanced Analytics BU, Advanced Analytics Sales - EMEA AP BU, SAS Software Ltd (United Kingdom)on ‘Maximising The Value of Analytics in Tax Compliance’ The presentation was made at Government Analytics & Information Summit 2013.

1. Copyr ight © 2013, SAS Institute Inc. All rights reser ved.
MAXIMISING THE VALUE OF ANALYTICS IN
TAX COMPLIANCE
IMAM HOQUE

2. Copyr ight © 2013, SAS Institute Inc. All rights reser ved.
ENTERPRISE
ANALYTICS
ENTERPRISE ANALYTICS ENVIRONMENT FOR TAX
Intelligence Repository
Data
Sources
Direct
Tax
Other Gov
Sources
3rd Party
Data
VAT
Analytics Environment
Customs
VAT Carousel
Tobacco / oils
/ contraband
Under-
declaration
Transfer
Pricing
Offshore
Evasion
Property Tax
Evasion
Social
Insurance
Healthcare
Fraud and Evasion Detection / Prevention / Management
Customer
Contact
Electronic
VAT
Reconciliation
Taxpayer
segmentation
Inspection
Effectiveness
Taxpayer
Sentiment
Cost
Optimisation
Risk
Management
Forecasting
Optimisation and Efficiency
Enterprise environment to support
an extensive range of business
applications

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TAX ANALYTICS ADOPTION OF BEST PRACTICE CONCEPT OF OPERATIONS
Tax Business Processes
Core system or
3rd party data
feeds
Advanced
analytics
Social network
analytics
Data ingest,
link and
enhance
Rules
Anomaly
detection
Scoring
MI &
Analytics
Special
InvestigationRescore
Events
People
Organisations
Alert / Case
Management
Events

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TAX ANALYTICS HOW TO GET THE MOST OUT OF ANALYTICS
Data
Tools and
techniques
Working models
for analysts
Models Operationalise Adoption
Continuous
improvement
Which business
problems?

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TAX ANALYTICS COMMON OBJECTIVES SEEN BY TAX AUTHORITIES
INCREASE DETECTION RATES
• Which businesses / people to inspect?
• Identify more sources of non-compliance
• Ensure fewer large cases go undetected
ACCURACY
• Reduce false positives – don’t waste time
• Focus on cases with higher yield
EFFICIENCY
• Work cases and inspections faster
• Optimise customer contact strategy
TOTAL COST OF OWNERSHIP
• Avoid high risk expensive custom developed
systems
A recognition that the
problem is
continuously changing
Move to develop “self-
sufficient” analytics
operations in house

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TAX ANALYTICS WHERE IS THE TAX GAP? (HMRC UK EXAMPLE)
Customer
contact
strategy
Debt
collection
Detect and
Inspect
Customer
contact
strategy

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TAX ANALYTICS WHAT ARE YOUR KEY TAX GAP CONSTITUENTS?
Tax
gap
Offshore
Spreading
salaries
Undeclared
property
Small
business
hidden
earnings
Businesses
never
registered
or don’t file
Bonuses
as
expenses
?

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TAX ANALYTICS APPROACH TAKEN BY LEADING TAX AUTHORITIES
Data Sources
Live and
batch input
data
Tax systems
Other Gov
Department
3rd party
public data
Financial
institutions
Out of
jurisdiction
Data Integration /
Entity Resolution /
Networked Data
Personal
Small
Business
Corporate
Analytics /
Detection and Alerting
Prioritized alerts
which cases to work
Hybrid Analytics
Model
End User Services
Reporting / explore
& search data
Case Management

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TAX ANALYTICS DATA INGEST AND INTELLIGENCE GENERATION
Data ingest
DI / DQ
SNA
Content Categoriser
Extract
entities
from text
Single
view of
entities
Link
entities &
records
Create
discrete
networks
Data integration tools allow data
sources to be rapidly integrated
and data quality to be
measured and addressed
Single views of entities are
produced through advanced
data matching techniques
Where free text exists within
documents or in record fields, it is
processed. Entities such as:
people, places, businesses,
addresses, phone numbers,
account numbers, etc. are
extracted automatically as entities.
All entities are exhaustively linked
together and large networks are
generated in the data
Filtering algorithms are used to identify
the discrete socially bounded networks,
such as fraud rings or crime gangs.
Statistics regarding these networks are
added
A comprehensive
intelligence
warehouse of all
claim and policy
history and
relationships

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TAX ANALYTICS A HYBRID MODEL IS REQUIRED TO DETECT TAX GAPS
Social
Network
Analysis
Database
Fuzzy
Matches
Text
Mining
Predictive
Modeling
Anomaly
Detection
Automated
Business Rules
PROJECTS ARE SIGNIFICANT SO ENSURE THE BEST MODELS ARE USED
Analytic
Decisioning
Engine

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TAX ANALYTICS CASES FOR INSPECTION AUTOMATICALLY GENERATED
Inspector’s work queue
High risk cases
Low risk risk cases

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TAX ANALYTICS MODELS SCORE ON 3 LEVELS
Event
Entity
Network
• Tax return
• Customer contact
• Change in circumstance
• Person or business
• Change in behaviour
• Entire history
• Entity outlier analysis
• 3rd party data
• All entities in a business
• Linked with known risk
• Network variables

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TAX ANALYTICS BETTER TO PROVIDE REASONS NOT JUST A SCORE
Risk of income under declaration
Income inconsistent with property post code
Income not progressing in line with inflation
Land registry indicates 4 properties
No rental income declared
Accountant links this person to others with
undeclared rental income
65%
Risk of income under declaration

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TAX ANALYTICS CASE WITH REASONS AUTOMATICALLY GENERATED
Data provided
to support
investigation
Reasons why
the case was
created
automatically
populated
Basic case
details
provided in one
place

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TAX ANALYTICS SNA NETWORK OF THE UNDERLYING DATA AND RISKS
Diagrams for
all cases are
automatically
generated by
the system

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TAX ANALYTICS SNA HAS DELIVERED THE LARGEST STEP CHANGE
Yield
POPULATION
█ ANALYTIC
DECISIONING ENGINE
WITH SNA
█ ANALYTIC
DECISIONING ENGINE
WITHOUT SNA
█ RANDOM
I.e. if you examine 50% of the
population, you would expect
to find 50% of the fraud
If the accuracy of a model doubles through the inclusion of network level variables alone,
this means an investigation team is able to find twice the amount of fraud with the same
number of referrals!

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TAX ANALYTICS STRUCTURED APPROACH TO ASSESSING THE PROBLEM
What you
know you
know
What you
know you
don’t know
What you
don’t know
you don’t
know
Existing debt
Projections
from known
case typologies
Few or no
examples,
projections
difficult
Optimise debt collection
and customer contact
Encode known MO’s as
models and execute
across data, sample
alerts and project
Outlier analysis.
Data exploration.
Learn from other Tax
authorities.
Learn from other
sectors.
Encode, sample and
project

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TAX ANALYTICS MODEL TUNING AND ACTION PRIORITIZATION?
Non-compliance
Alerts
• Multiple models for different non-compliance
typologies
• Generate alerts for potential non compliance
Segment by
propensity to
treatment types
• Post
• Call centre
• Inspection
Yield
• Optimise for likelihood to
recover
• Optimise for fiscal value
Deterrent impact
• Geographic
• Typology
• Key influencers

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TAX ANALYTICS OTHER EXAMPLES: DEBT COLLECTION OPTIMISATION
HMRC – +20% resource savings, immediate cash flow benefits, call centre improvements
Swedish Tax Office – 30%-75% resources savings across different channels
Philippines Bureau of Internal Revenue – 10% on collections
• Segment Characteristics
• Control-based Selections
• Recommended Information
• Recommended Channel
• Scheduled Execution

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TAX ANALYTICS SUCCESSFUL ADOPTION APPROACH
• Establish super user community
• Involve them in strategy, plans and model designInvolve users
• Start simple, let initiatives bed in
• Ramp up sophistication as a programme of workIt’s a journey
• Don’t leave users guessing “Why?”
• Where possible get the most out of transparent toolsAvoid black box
• Ensure you pick challenges where results are easy
• Simulate first, limit roll-out until provenFocus on results
• Give users the data they need, make them efficient
• Think carefully about delivery mechanismsEnable users
• Always gauge reaction and enhance
• Structured plan for roll-outs, training, etc.Pilot and iterate

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TAX ANALYTICS MAKE USERS MORE EFFECTIVE
Fewer false positives:
- Avoid wasted time
Case file
- Rapidly understand case
Data exploration
- Quick and easy first pass
investigation
Initial
contact
Work
Case
Evidence
file
Rapid
triage
Dropped Cases – occur earlier in the cycles with less work
Yield
based on weight
of evidence alone
Yield
Maximum effort
required
Model
%
%
% %
%
%
Percentages
determine
efficiency
%
%

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TAX ANALYTICS A JOURNEY TO TAX COLLECTION EXCELLENCE
Capability
Yr1 Yr2 Yr3

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FPS FINANCE (BELGIUM) - VAT CAROUSELS
Business Problem
VAT Carousel fraud was a billion euro problem for the Belgian Government. This fraud typology is a high velocity
fraud: “Carousel frauds are like floods. It is futile to believe that after the storm we can put water back in the riverbed
with buckets. What is needed is to build dykes to prevent overflows”.
The Tax department is overloaded with data relevant for VAT Carousel Fraud (600.000 tax-payers, 5.000.000 VAT
returns, 24.000.000 Intra-community transactions).
SAS Approach
The solution is used to detect companies with a high probability of being involved in VAT Carousels. The SAS
Hybrid approach provides ultra-early detection as from the first suspect VAT return or other suspicious behaviour.
The models are highly accurate (80% true positive rate). This makes the SAS Hybrid approach the ideal tax auditor.
Results
The result is a reduction by 98% (from 1.1 billion € to 0.029 billion €) of the VAT Carousel fraud. VAT Carousels are
now a controlled phenomenon.
Highlights
• VAT Carousel fraud reduced by
98% (from 1.1 billion € to 0.029
billion €)
• Ultra-early detection
• SAS hybrid approach
provides a high accuracy
model (80% true positive rate)

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Missing Trader1
VAT IN=0
VAT OUT=21
Due VAT=21
No VAT is paid by the supplier on
an intracom delivery. The VAT is
due in the destination country. The
Missing trader disappears without
paying the due VAT
Profit Taker
VAT IN=21
VAT OUT=0
Due VAT=-21
No VAT is due by the Profit Taker
as the goods are shipped out of
the country via an intracom
delivery. The VAT “paid” by the
profit taker (21) is claimed back
from the Tax agency.
Value of goods: 100
VAT: 21%
Societe Buffer
VAT IN=21
VAT OUT=21
Due VAT=0
The buffer company only
exists to hide the Missing
Traders from the Profit Taker
Belgium
France
TAX ANALYTICS VAT CAROUSEL FRAUD

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QUESTIONS?
IMAM.HOQUE@SAS.COM