> VAT Fraud Challenges
• Fiscal Drain: Value-Added-Tax fraud and noncompliance is draining finances of many
countries —European Union losses estimated at
over 160 billion euros per year
• Recovery of losses: VAT fraud patterns are
difficult to detect
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–
–
–
1
Take place over time
Frequently involve complex sets of relationships
Hidden within huge volumes of transactions
Constantly change
> Tax Intelligence System (TIS) Meets
VAT Fraud Challenges
• ASAP detection: Helps detect fraud
situations as soon as transaction data is
available – whether it is real time, that day
or that week
• AI driven: Applies hybrid AI technologies
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–
–
–
2
Analyzes huge volumes of time-series transactions
Alerts tax authority staff to suspect situations
Assists in prioritizing responses
Highly adaptable reasoning logic keeps up with
changing fraud patterns
> VAT Fraud Solution Benefits
• Higher tax revenue: Targeting recovery of 25% to 50%
of lost VAT revenue
• Improve productivity: Increase effectiveness of VAT
fraud staff
– Prioritize inspections and audits
– Minimize costly false positives
• Fairer competition: Help ensure fair competition by
collecting VAT owed equally from all businesses
• Minimize VAT rates: Help maintain lowest possible VAT
rates
• Starve fraudsters: Cut cash to those committing fraud
3
> TIS AI-Driven Technologies
• AI software agents scale to analyze many
millions of daily transactions in real time or as
batches
• Structured natural language rules
– Updateable by tax analysts without need for IT support
– Enable explanations of conclusions to support decisions
• Hybrid AI techniques help detect and predict
increasingly complex fraud:
– Bayesian probability networks for reasoning
with uncertain evidence
– Machine learning for classifications
– Semantic reasoning for analyzing networks
of transactions
4
> TIS Architecture Overview
Geographical
communication
Azure
Event
Hubs
Disparate
Data
Sources
3rd Party Systems
Semantic Interoperability
across heterogeneous
systems
Azure
Stream
Anal.
Visualizations, machine learning, simulators,
Business Intelligence…
Distributed processing
Cognitive Reasoning Engine (CRex)™
Semantic knowledge
base fuses data &
systems
Big Data storage and
processing
Azure
Apps
AI Agents apply
time-series
reasoning
Low-latency, real-time
reasoning and actions
Azure
Machine
Learning
Reasoning over time-series
data
Economic-driven
decisions
Results
Power
BI
5
Situational Management of
VAT fraud and non-compliance
Azure
Notif.
Hubs
> VAT Fraud Reference Example
• Customer: Ceará, a state in Brazil
• Estimated revenue loss:
80 M USD / year
• Targeted detection rate: >50%
• Number of tax laws to be applied
in real time: approximately 200
• Time to implement: 3 to 9 months
for similar projects, depending on
scope
• Prerequisites: digitized invoices
6
2,000,000
transactions
per day from
60,000
vendors,
200,000 rules
per second