Preventing Tax Evasion & Combating Fraud through Predictive Analytics

Preventing Tax Evasion &
Combating Fraud
through Predictive Analytics
Evert Voorn | Thought leader,
Tax & Welfare, Capgemini
Seminar Digital Transformation
May 12th, 2015 | Stockholm (SE)

2Copyright © 2014 Capgemini. All rights reserved.
Stockholm, May 12th 2015
The world is changing and this drives the need for a different model of risk
management
External
trends
 Push to online applications/change of circumstances and renewals; use of mobile apps / text;
voluntary sector support for “needs help” segment
 Make it simpler for customers so that they comply with the need to inform of change of
circumstances and make fewer genuine errors. Design out contact; simplify online forms & guidance
and processes
 Welfare administrations are leveraging new data sources and more sophisticated modelling tools and
data mining to improve targeting of high risk cases, detection of fraud and retrieval of debt
 Segmented approach to fraud investigations; graduated range of interventions; moving upstream
(prevention)
 Process standardisation across benefits; re-platforming; shared services, performance KPIs to
drive productivity
 Outsourcing /JVs of selected functions e.g. IT, debt recovery, analytics; new commercial
models
 Growing use of internet to research and buy private sector products and services – fuelling 24/7 and
e/mobile app service expectations
 Explosion in the use of social media – ability to build customer insight but reputational risks
Internal
Trends
(government
e.a.)
Digital by default
Improve customer
experience
Data analytics to
better target risk
Re-inventing
government
administrations
Growing use of
digital
Globalisation
Industrialisation of
fraud
 Tax competition between states - large business tax domiciles; inward investment; key skills
 Rapid growth of emerging economies and middle class – demand for welfare state
Financial austerity
in the West
 Stagnating or declining real incomes in the West; fiscal deficits and levels of debt
 Political pressure to address tax non-compliance and welfare fraud
 Increasing sophistication of banks and insurance companies are driving criminals to attack tax & welfare
authorities; testing defences; insider fraud
Technological
developments
 Big data - new data sources e.g. Social media, smart grid; processing power e.g. Hadoop;
 High performance analytics e.g. SNA, voiceprints – shorter analytical timescales
 Internet transparency

Is there really a fraud problem?
It is estimated that
approximately €100 billion
in total is involved in the
wrongful
non-payment of VAT
within the EU Member
States each year
Source: EU MTIC Report
Shadow economies are
estimated to have
accounted for £880 billion
in lost tax in the EU
between 1999 and 2007
Source: tax justice network
It is estimated that MTIC
VAT fraud contributed
between £0.5 billion
and £1.0 billion to the
UK VAT gap in 2010-11.
Source: HMRC report (2012)
Measuring tax gaps 2012; Tax gap
estimates for 2010-11.

The fraud landscape spreads from simple
opportunistic non-compliance to organized crime
Opportunistic Premeditative
Casual
Avoiders
Purposeful
Criminals
Organised
Crime
High Volume/Low Value Low Volume/High Value
Systemic
Game
Playing
Opportunistic
Level of Sophistication of Fraud
Taxpayer
Segment
Fraud
Type

In response, a differentation to type and impact of fraud patterns is needed to
determine which counter measures must be applied (and at different point of the
business process)
Opportunistic Premeditative
Average Insurance
Fraud
Criminal Offender Organised
Crime
High
Volume/
Low Loss
Low
Volume
/High
Loss
Systemic
Game
Playing
Opportunistic
Taxpayer
Segment
Fraud
Type
Anomaly detection
Predictive Modelling
Social Network Analysis
Text mining
Database Searching
Business rules
Complexty of Fraudulent patterns

Issues
Detect high volume
fraudulent behaviour
Discover new
fraudulent patterns
Identify organised
fraud networks
Set up rules to filter
fraudulent
transactions
Examples:
Taxable income less
than previous year,
return is late etc
Search database of
known or suspected
fraudsters using data
matching algorithms.
Examples:
Known fraud
addresses, previous
proven cases,
informants
Use Statistical
analysis to detect
cases where
behavioural patterns is
distinctly different from
the norm
Examples:
Clustering, peer group
analysis, trends, linear
regression , outlier
identification
Modelling to identify
sophisticated and well
disguised fraudulent
behaviour
Examples:
Neural Networks,
decision trees,
multiple regression
Visualising the nature
of relationships
between individual
entities
Examples:
Social network and
linkage analysis,
community detection
Identify hidden
patterns and
inconsistencies in
unstructured data
such as claim forms,
letters
Examples:
Scripted word or
phrases used by
multiple individuals
Suitable for
known patterns
Suitable for
known fraud
Suitable for
unknown patterns
Suitable for
complex patterns
Suitable for
associations
Suitable for
unstructured
patterns
Rules Database Searching Anomaly Detection Advanced Analytics Social Network Analysis Text Mining
Hybrid Approach
Combination of approaches used to score and prioritise cases for investigation
Statistician Each of the techniques is powerful on their own. But when overlaid as a hybrid
approach it enables statisticians to inform a comprehensive and targeted approach
to identifying potentially fraudulent TRANSACTIONS, ENTITIES, AND NETWORKS
ACROSS MULTIPLE ORGANIZATIONS
Different analytical techniques are applicable either on their own or in
combination, dependant on the issue to be adressed
The Advanced Risk Detection Toolbox contains lots of candy…

The non compliance Maturity Journey of modern (Public) Organisations
Specialist
Investigators
In-Process
Controls
Expert Rules
Advanced
Analytics
Build special
Investigation Unit
focused on non-
compliance
Implement in-
process controls to
stop and catch non
compliance
Apply exception based
rules, including tip-offs,
to identify suspicious
behaviour
Use statistics to
predict future non-
compliance
Time
NonComplianceMaturity
Our client’s
Current
position (?)

The transaction based industries will need to move from ‘checking’ to ‘risk based’
analytics....
Up-front data
matching accuracy
and eligibility
checks
Pre-emptive and
initial risking
Synthesis of risk
and case
prioritisation
Sophisticated,
algorithm-based
response
Compliance rules Risk rules Risk score Risk-based treatment
Individual reports
income ‘A’ and
compliance rule is used
to compare it to known
income value ‘B’
reported by employer
Individual reports
income ‘A’, risk rule is
used to assess the
propensity to risk, e.g.
by comparing income to
possession of assets
Individual triggers
multiple (risk) rules
which are combined into
single risk score that
enables the Agency to
differentiate between
the level of risk between
individuals
Individual triggers
multiple risk factors and
based on predictive risk
score, this individual is
treated differently
CharacteristicsExample

But it is how you analyse the data that is key to future success
Business
“Business” – it is the use of
analytics to directly target a business
issue or process and as such is sold
to the Business. Examples are
customer retention, increasing wallet
share, fraud reduction…
Business Analytics is the uses of advanced analytical techniques to find
trends and predict future outcomes which are used to optimize
business processes, customer interaction and manage risk and fraud.
Analytics
“Analytics” – it makes extensive
use of data, statistical and
quantitative analysis, explanatory
& predictive modeling, and
fact-based management to drive
decision making.
Governments will have to become data-driven, analytics-enabled organisations

Capgemini’s approach for combating fraud and error (Trouve)
applied to Tax Agency business processes
Business Value Chain
Register
/Change of
Details
Process
Application
/Return
Establish
Liability
/Benefit
Manage
Payments
In/out
Reconcile
Investigation
/Audit/
Enforcement
Enforcement
/Debt collection
/criminal
proceedings
Receive
Customer
Submission
Downstream ProcessesUpstream Processes
Upstream Downstream
Processing embedded Processing independent
 Segmented risk based approach
 Risking by taxpayer
 Data matching; use of sophisticated analytical
techniques and visualization
 Case referral and workflow
 Graduated set of compliance interventions
 Focus on prevention
 New capabilities – identity assurance; predictive models
 Denial of service techniques
 Real time re-profiling & personalized ‘nudge’ techniques

B
An example of the use of advanced analytics in combating fraud:
VAT Carousel Fraud – Basic Pattern...
Process Diagram
A
CCDD
EE
Country 1
Country 2
Tax
Authority
VAT due
VAT
reclaimed
Buffer Traders
Missing Trader
Criminal Attack
A sells goods to B for
€1m. No VAT as A is in
different EU country
from B
E sells goods to A for €950k. No VAT
due as A is based in another EU
country. C claims refund of VAT of
€180k charged by B
1
B sells goods to C for
€900k plus €180k VAT.
B never pays €180k VAT to
Belastingdienst and
"disappears"
2
3

VAT Carousel Fraud – Presented Pattern

VAT Carousel Fraud – A network after investigation

VAT Carousel Fraud – Getting the picture…

15
Then: moving from Investigating networks…

..... To event oriented risk assessment (predictive analytics)
16
Amount
2 - 4 months
Current
situation
SAS Fraude Framework
Preventive
First
verification
First ICA Tax Return
Payment
Subject
Time

Results: Breaking News! -
Revolution within Tax Agency in combating fraud
Secretary of State for the Treasury stated in
parliament 8 october 2014: “there is a
small revolution going on, apllying data-
anlysis and automated profiling”

The information contained in this presentation is proprietary.
Copyright © 2014 Capgemini. All rights reserved.
Rightshore® is a trademark belonging to Capgemini.
www.capgemini.com/bim
About Capgemini
With more than 130,000 people in over 40 countries, Capgemini
is one of the world's foremost providers of consulting, technology
and outsourcing services. The Group reported 2013 global
revenues of EUR 10.1 billion.
Together with its clients, Capgemini creates and delivers
business and technology solutions that fit their needs and drive
the results they want. A deeply multicultural organization,
Capgemini has developed its own way of working, the
Collaborative Business Experience™, and draws on Rightshore®,
its worldwide delivery model.

Preventing Tax Evasion & Combating Fraud through Predictive Analytics

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