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Smart Solutions: Data Analytics Substantial to Support Fraud Investigations
 

Smart Solutions: Data Analytics Substantial to Support Fraud Investigations

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This presentation illustrates proven data analytics workflows applied in various types of investigations, and how to establish them to make your investigations more efficient and effective. ...

This presentation illustrates proven data analytics workflows applied in various types of investigations, and how to establish them to make your investigations more efficient and effective.
You will learn well-proved data analytics workflows, including understanding, cleansing, optimizing, analyzing your data and reporting the results.

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    Smart Solutions: Data Analytics Substantial to Support Fraud Investigations Smart Solutions: Data Analytics Substantial to Support Fraud Investigations Presentation Transcript

    • Smart Solutions: Data Analytics to Support Fraud Examinations
    • About me Understanding data Cleansing data Enriching and validating data Importing data Analyzing data Reporting Agenda 2
    • 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 3
    • About corma GmbH 4 Stops suspects by: analytical investigations operative investigations Saves time by: online research online monitoring Increases efficiency and saves money by: data analytics global intelligence solutions
    • Data Modeling 5 © corma GmbH
    • Workflow Understanding data Cleansing/standardizing data Enriching and validating data Importing data Analyzing data Reporting What Are “Smart Solutions?” 6
    • We need to understand data related to our cases. Which data? Understanding Data 7
    • It is a challenge to understand data. What kind of challenge? Data quantity Understand relationships and background Bring data into context How does it work? In four steps Understanding Data 8 © Dan Roam
    • Look at the data: Understanding Data 9 © Dan Roam
    • See the pattern: Understanding Data 10 © Dan Roam
    • Imagine: Understanding Data 11 © Dan Roam
    • Show: Summarize your findings Understanding Data 12 © Dan Roam
    • What did we accomplish? Understanding Data 13
    • corma Workflow in 3 Steps 1. Chain of custody a) Record all your steps i.e., in a Word document Software: CaseNotes, OneNote by Microsoft b) Store original data in a secure area c) Create digital fingerprints: MD5 Hash http://md5deep.sourceforge.net www.bitdreamers.com (Checksum Verifier)  Compare file content (UltraCompare) d) Work with a copy of the original data only Understanding Data 14
    • 2. Identify data formats a) Research www.file-extensions.org www.filext.com www.fileinfo.com .gpi .bqy .blb Understanding Data 15 Garmin Point of Interest file BrioQuery database file ACT! database file
    • 2. Identify data formats b) View (read only) www.uvviewsoft.com Understanding Data 16
    • 2. Identify data formats c) Deep view (editable) www.ultraedit.com Understanding Data 17
    • 3. From raw data to smart structured data Understanding Data 18 Develop first ideas for analytical approach
    • Understanding Data 19 First import and analytics
    • Understanding Data 20 Result: Identified and understood data Data preparation
    • Workflow Understanding data Cleansing/standardizing data Enriching and validating data Importing data Analyzing data Reporting What Are “Smart Solutions?” 21
    • Challenges High data quality required for good analysis results Constantly increasing data quantity Cleansing/Standardizing Data 22
    • “Bad data” samples Cleansing/Standardizing Data 23
    • Why should data be cleansed: Reliable analysis results are required. Data cleansing saves time that otherwise would come up during the analysis process. Reduce unwanted deviations and variations. Identify entities (e.g., person, organization, address). Insights often lead to further findings. Cleansing/Standardizing Data 24
    • Fast and flexible handling of large quantities of data Flexible import from various data sources Intuitive research Analyses, calculations, statistics Business Intelligence Ad hoc reporting 25 Solution
    • Combine different data formats Fix data quality issues Identify missing data Optimize link analysis results 26 With InfoZoom you can
    • 27 Benefits Benefits: Time-saving Flexible Maximize effectiveness Team “compatibility” Easy to learn By means of: Developed workflow for recurring processes Standardized processes (templates)
    • Workflow Understanding data Cleansing/standardizing data Enriching and validating data Importing data Analyzing data Reporting What Are “Smart Solutions?” 28
    • Imagine: Enriching and Validating Data 29
    • Geocoding: www.gpsvisualizer.com Enriching and Validating Data 30
    • Whois query - manually Enriching & Validating Data 31
    • Whois batch query Enriching and Validating Data 32
    • Whois Enriching and Validating Data 33
    • Whois Enriching & Validating Data 34
    • Address verification—manually Enriching & Validating Data 35
    • Address verification—service provider or software (for large amounts of data): AddressDoctor www.addressdoctor.com Experian www.qas-experian.com.au Enriching & Validating Data 36
    • Workflow Understanding data Cleansing/standardizing data Enriching and validating data Importing data Analyzing data Reporting What Are “Smart Solutions?” 37
    • Importing Data 38
    • 39 Sample Import: i2 IBM-Database
    • 40 Case Study: Insurance Claims Audit One file ready for analysis
    • Workflow Understanding data Cleansing/standardizing data Enriching and validating data Importing data Analyzing data Reporting What Are “Smart Solutions?” 41
    • 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? How to create evidence out of data? Visualize your thinking! Analyzing Data 42
    • Analytical techniques Chronologies and timelines (understand timing and sequence of events) Sorting (categorizing and hypothesis generation) Ranking, scoring, prioritizing (determine which items are most important) Network analysis—analyze relationships between entities (e.g., people, organizations, objects) Analyzing Data 43
    • Best practice: Document processes in intranet/wiki Select the right tool for each task Train the users Keep the users “busy” Look out for new solutions Analyzing Data 44
    • Query—an investigative question, converted into database search Analysis Sample i2 IBM 45
    • How many organizations are known at this address? Analysis Sample i2 IBM 46
    • 47 Email Analysis with Intella
    • 48 Timelinemaker i2 IBM Analyst’s Notebook Timeline Charts
    • 49 Classic view: Event log View: Event log Explorer Windows Event Log Analysis
    • 50 Windows Event Log Analysis
    • Workflow Understanding data Cleansing/standardizing data Enriching and validating data Importing data Analyzing data Reporting What Are “Smart Solutions?” 51
    • Final work starts when single components are ready: Reporting the Results 52
    • Reporting the Results 53
    • 54 Jörn Weber—jw@corma.de +49 (162) 1009402 corma GmbH · Heinz-Nixdorf-Straße 22 · D-41179 Mönchengladbach · Tel: +49 2161 277 85 - 0 · Email: mail@corma.de · Web: www.corma.de Thank You!