Smart Solutions: Data Analytics to Support Fraud Examinations

Smart Solutions: Data Analytics to

Support Fraud Investigations

About me

Understanding data

Cleansing data

Data validation & enrichment

Importing data

Analyzing data

Reporting

Monitoring

Agenda

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Jörn Weber

Certified Fraud Investigator

19 years experience - German law

enforcement

since1999 Managing Partner at

corma GmbH:

Solution provider

Partner for corporate security

About me

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About corma GmbH

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Stops suspects by:

analytical investigations

operative investigations

Saves time by:

online research

online monitoring

Increases efficiency &

saves money by:

data analytics

global intelligence

solutions

Data Modeling

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© corma GmbH

Workflow

Understanding data

Cleansing / Standardizing data


Importing data

Analyzing data

Reporting

Monitoring

What are “Smart Solutions”?

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Understanding data

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It is a challenge to understand data

What kind of challenge? Data quantity

Data access

Data integration

Understand relationships & background

Bring data into context

Understanding data

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© Dan Roam

How does it work? In four steps

Understanding data

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© Dan Roam

Look at the data:

Understanding data

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© Dan Roam

See the pattern:

Understanding data

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© Dan Roam

Imagine

Understanding data

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© Dan Roam

Show – summaries your findings

Understanding data

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© Dan Roam

Understanding data

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1. Chain of Custody

• Record all your steps

Software: Hunchly https://www.hunch.ly/

Plain document

• Store original data in a secure area

• Create “digital fingerprints”: MD5 Hash

• Work only with a copy of the original data

corma Workflow

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https://www.hunch.ly/

2. Identify data format

• Research http://www.file-extensions.org

http://www.filext.com

http://www.fileinfo.com

.gpi

.bqy

.blb

Understanding data

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Garmin Point of Interest file

BrioQuery database file

ACT! database file


• View (read only) http://www.uvviewsoft.com

Understanding data

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• Deep view (editable) http://www.ultraedit.com

Understanding data

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3. From raw data to smart structured data

Understanding data

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Develop first ideas for analytical approach

Understanding data

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Identified & understood data

Understanding data

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First import & analytics

Workflow

Understanding data



Importing data

Analyzing data

Reporting

Monitoring


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Challenges

High data quality required for good

analysis results

Constantly increasing data quantity

Cleansing/Standardizing data

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“Poor data quality” samples


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Why should data be cleansed:

Reliable analysis results are required

Saves time that otherwise would come

up during the analysis process

Reduces unwanted deviations &

variations

Identify entities (person, organization,

address)

Insights often lead to further findings


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Fast and flexible handling of large quantities of data

Flexible import for various data sources

Intuitive research

Analyses, calculations, statistics

Business Intelligence

Ad-hoc reporting

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Solution

Combine different data formats

Fix data quality issues

Identify missing data

Better link analysis results

Application of different tools for standardized data cleansing

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Solution

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Solution

Develop automated queries

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Benefits

Develop workflow for recurring

processes

Standardize processes (templates)

Benefits:

Time saving

Flexible

Maximize effectiveness

Team “compatibility”

Easy to learn

Workflow

Understanding data



Importing data

Analyzing data

Reporting

Monitoring


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Imagine


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Geocoding: http://www.gpsvisualizer.com


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Whois (historical records)


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Relationships between Entities


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Visualization & link analysis


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Address verification – manually


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Address verification – service

provider or software for large amounts of

data

AddressDoctor http://www.addressdoctor.com

Experian http://www.qas-experian.com.au


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Workflow

Understanding data



Importing data

Analyzing data

Reporting

Monitoring


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Importing data

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Sample import: i2 IBM-Database

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Case study:

Insurance claims audit

Workflow

Understanding data



Importing data

Analyzing data

Reporting

Monitoring


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Analytics … yes … but structured:

Identify needed analytical steps

Develop „questions“ to data

What has prompted the need for the analysis?

What is the key question that needs to be answered?

„Create“ evidence out of data

What can you prove?

What do you want to prove?

Visualize your thinking!

Analyzing data

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Analytical techniques

Chronologies and timelines (understand

timing and sequence of events)

Sorting (categorizing & hypothesis

generation)

Ranking, scoring, prioritizing (determine

which items are most important)

Network analysis – analyze relationships

between entities (people, organizations,

objects)

Analyzing data

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Supporting tools:

Documenting processes in intranet/wiki

Select the right tool for each task

Train the users

Keep the users “busy”

Analyzing data

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Query - an investigative question,

converted into database search

Analysis Sample: i2 IBM

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How many organizations are known at

this address?

Analysis Sample: i2 IBM

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Analysis Sample (InfoZoom)

Decoding (classification, i.e. phone data)

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Email analysis with Intella

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Timelinemaker

i2 IBM Analyst‘s Notebook

Timeline Charts

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Classic view: Event log

View: Event log Explorer

Windows event log analysis

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Windows event log analysis

Workflow

Understanding data



Importing data

Analyzing data

Reporting

Monitoring


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Final works starts when single

components are ready

Reporting

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Reporting

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Workflow

Understanding data



Importing data

Analyzing data

Reporting

Monitoring


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Proactively maintain a high, consistent

standard of data quality

Monitoring

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Jörn Weber - [email protected]

+49 (162) 1009402

corma GmbH · Hochstr. 2 · D-41379 Brüggen·

Tel: +49 2163 349 0080 · E-Mail: [email protected] · Web: www.corma.de

Thank you!

Data & Analytics

Smart Solutions: Data Analytics to Support Fraud Examinations