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Identifying fraudulent shipments through matching
shipping documents with phone records
CzesawJdrzejek
MISSI 2012
With M. Nowak and M. FalkowskiInstitute of Control and Information Engineering
Poznan University of Technology
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Agenda
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1. Motivation2. Goals
3. The model
4. Rules and queries
5. Conclusions and Future Work
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Motivation Presentation of analytic approach to complex legal problems
This results in lack of analytic tools able to go beyondcollecting facts and performing some form of visualization onthese facts for a complete case
According toACFE economic crimes cost typical organization
5% of its annual revenue.
Economic crimes, 300-600 types, are particularly difficult tomodel and code into an expert system (contrary to a medicalarea, where knowledge appears mainly in form of
taxonomies). For example, fraudsters use many types ofschemes, techniques and transactions to achieve their goals,so it seemed impossible to construct a simple conceptualmodel of any generality
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Goal
Construct minimal ontology of a typical model (assetmisappropriation constituted two-thirds of all economiccrimes, PWC 2009 survey) we selected fraudulentdisbursement
Develop methodology ofanalysis
Generalize to a larger category of economic crimes
Design and implement a tool (AFIZ, Analyzer of Facts andrelations)
Verify with legal experts
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Motivation
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Despite enormous effort devoted to theconstruction of top level, core legal andapplication ontologies, there exists avery small number of works resulting indevelopment of practical systems
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Goals
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We want to decide whether a shipment to Czech
Republic/Slovakia could occur as stated in CMRdocuments.
Whether the driver could have reached the
destination point in Czech Republic/Slovakia by 15
oclock on day T2
Whether the driver could have reached the
destination point in Czech Republic/Slovakia on day
T2
Whether the driver could have reached the goods
receiving place in Poland on day T3, if known.
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The model
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We extract data from CMR documents to create
objects which correspond to origin and delivery
locations of the shipment.
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Transport day
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Transport day:
Criteria 1
Criteria 2
Criteria 3
Then 3 location objects form the transport day, which isthe main object of our analysis.
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Phone records convertion
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We convert phone records into phone calls objects withGIS property (of its corresponding BTS tower).
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Automated analysis
All objects are then moved into the map application.
Our system separates them by date, and performs day
by day analysis
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The algorithm
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Conclusions and Future Work We extended the minimal economic crime minimal model to a
widertypology
The importance of this work is not in formal progress in reasoning
but demonstrating that it is possible to build ontologies for
nontrivial cases
It is important to ask why in law area we deal with such a detailed
description is possible? The reason is that there is no
unintentional fraud, and evidence we considerare documents and
bank accounts, something that is traceable.
We hope that the knowledge base we develop will be amenable to
extensions that would make it highly practical
We are working on implementation of the knowledge base and
generation ofartificial data to test our approach 13
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THE END
Thank you for your attention!
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