Fraud Detection How to Use Big Data and Real-Time Decisions to Combat Fraud Jan Beck, Principal Solution Architect Tom Petrash, Executive Director Industry

Fraud DetectionHow to Use Big Data and Real-Time Decisions to Combat Fraud

Jan Beck, Principal Solution ArchitectTom Petrash, Executive Director Industry Solutions GroupAndre van der Post, Global Director Public Sector Revenue Management

Public Sector Revenue ManagementOctober 1, 2014

Copyright © 2014, Oracle and/or its affiliates. All rights reserved. |

Copyright © 2014, Oracle and/or its affiliates. All rights reserved. | 3

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“UK's tax gap rises in 2013 by £1bn to £35bn ($57bn)”- Article in The Guardian, Friday October 11, 2013

$57billion


Percentages are estimatesUK Tax Gap in 2013 ($57bn)

15%

13%

11%61%

Tax Gap reasons

Tax evasionCriminalAvoidanceOther

33%

44%

13%

7%

3%Taxes affected

VATIncome taxCorporation taxExcise dutiesOther

5


“The Perception Constituents and Companies have that a Tax Authority has all ‘Contra Information’ available and using it, is shifting.”

– Peter Lehr, R&D Architect, Dutch Tax Authority, 2014


• Is it worth spending $500m to stop $50m fraud?

• Is it worth spending $500m to stop $50bn fraud?

• The Pareto Principle— The first 50% of fraud is easy to stop;— The next 25% takes the same effort;— The next 12.5% takes the same

effort;— The next …

Resources available for Fraud Detection

Revenue collected

Cost of combating Fraud


A working modelTax Authority – Taxpayer Fabric

8

Interaction with Tax Authority

Processing

Analytics

Arena where Fraud takes place


Analytics – Great, but…

• Fraud has already taken place, so money is gone…• Models are based on historic perspective of non-compliance• Models tend to be static and tend to be difficult to change/update• Models do not take into account the changes in Taxpayer behavior

associated with non-compliance• Most fraud systems deliver an overwhelming amount of false positives• What about the underground economy?

We need more than the ability to find the “Needle in the Hay Stack”

9


From Factors influencing constituent behavior to Agency actionsCompliance

Effect on compliance Defined Compliance scale

Type of actions to be taken

Internal factors

External factors

10


In plain sight, obscured by similar looking marblesWhere is the best place to hide your marbles?

11


Not every Fraudster is the same

• Many different fraud strategy groups (A..Z and beyond)– Apple from the crate: Isolated (smaller) incidents and hope the Revenue Agency does

not notice– Bonnie & Clyde: Get as much as possible, as quickly as possible – Chameleon: Blend in and act as normal as possible (too ‘Normal’?)– Double barrel: (A-)synchronous intertwined VAT Carrousels– E…

• Each of the above (not limited by the alphabet) may have many variants• Fraudsters do not generally give up. They change strategies!

How many Strategies to anticipate?

12


The Concept

• Build the model– Capture behavioral aspects in models of ‘normal’ compliant Taxpayers– Capture relevant data on selected aspects (Structured and unstructured (both Big Data))– Create benchmarks (Data Mining (on Big Data); Population of scorecard templates)– Test drive with a feed-back loop into a Decision Engine (Enhance the Models)

• Run it!– Reflect current behaviors and profiles against the “Compliant Taxpayer” model and the absence of

“normal” behavior is a pointer to non-compliance– Continuous learning / improvement through Feed-back loop with a Real Time Decision Engine

“One-class approach” – a Compliant Taxpayer

13


“Fraud management requires a holistic approach, blending tactical and strategic solutions as with the state-of-the-art technology solutions and best practice in fraud strategy and operations.”– James Gilmour, Editor Credit Risk International, 2003


Aggregation from multiple sourcesData Collection and Processing

Social MediaWebsites

Uns

truc

ture

d

Big DataContent Systems,

Files, Email

OLTP & ODSSystems

Enterprise Applications

Str

uctu

red Data Warehouse

& Data Marts

Stream OrganizeAcquire DecideAnalyze

15

ExalyticsExadata Big Data Appliance


The most important functionsOracle Advanced Fraud Management


Enables Adaptive Business Processes(Real Time) Decisions and Advice as a Service

Suppliers

Decision Data

Process leverages common data model of real-time and historical dataDecisions based on facts, context and analytic insights

Decision Request

• Recommendations with rules & predictive models

• Learning from each interaction

• Process Optimization across business process goals

• Process learns and continuously optimizes in real-time or batch based on closed loop information

• Analytical decisions for each interaction

Final Decision Feedback loop

Advice / Decision

17


Oracle Advanced Fraud ManagementFunctions overlaid with Oracle products

18

PSRM Analytics

Endeca Info Discovery Real-Time DecisionsMaster Data Management

Oracle Big Data Appliance

Exadata

Oracle GoldenGate Oracle Data Integrator

Exalytics

19Copyright © 2014, Oracle and/or its affiliates. All rights reserved. |

and answers…Questions



Backup slides


How to Use Big Data and Real-Time Decisions to Combat Fraud

Traditional fraud detection has not been particularly successful, largely because it happens after the fact (at times, as long as two years after an incident). New thinking needs to be injected into the strategies for dealing with fraud. Through the use of cutting-edge technology that brings transactional, analytical, and big data together in real time, Oracle’s new approach can detect deviant behavior much earlier, enabling revenue agencies to combat fraud in a much more effective way.

23


Compliance

• Tax Compliance is when a taxpayer Files for and Pays the correct amount of tax, (calculated correctly on the correct Tax Base for Assessment) in the correct place at the correct time.

• Correct means that the economic substance of the transactions undertaken coincides with the place and form in which they are reported for taxation purposes.

Definition we use


The path from Compliant to Fraudulent The compliance scale

Normal Optimization(Avoidance)

Difference in interpretation

of the law

Unintended mistakes

Intended mistakes Evasion Fraud

Tax Compliance Tax Non-compliance

25


In the underground economy millions of firms are engaged, employing hundreds of millions of workers and producing trillions of dollars of output internationally.

10%


Conspirators have made a profit of £500,000 perfectly legitimately on buying and selling mobile telephones.

But now:

• The first business (B) vanishes without paying the VAT to HMRC.

• When the last business in the chain collects £240,000 on the export, all of the businesses can vanish, £220,000 better off at the expense of HMRC

As this business is removed from the vanishing party, it is hard for HMRC to show the links in the chain and thereby could refuse to refund the VAT on the export.

VAT Carrousel (Fraud) Example for Europe

B

A

Telephones

1,000,000 +0 VAT

C

1,100,000 +220,000 VAT

D

1,200,000 +240,000 VAT

E1,500,000

Telephones


AnalyticsData Sources incl. Big Data

Master Data Management

Operational System(s)

Constituent Self-Service

Compliance Director with

Real-time Decisions

Information Discovery

Documents

Fraud Detection How to Use Big Data and Real-Time Decisions to Combat Fraud Jan Beck, Principal Solution Architect Tom Petrash, Executive Director Industry