MAKING SENSE OF DATA - IIA FIJI · • Report Informed Decisions Traditional risk based analytics....
Transcript of MAKING SENSE OF DATA - IIA FIJI · • Report Informed Decisions Traditional risk based analytics....
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Shagen Ganason, CIACAE, Te Puni Kōkiri (Ministry for Māori Development)
MAKING SENSE OF DATAAnalytics in Action
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Agenda
• Context
• Analytics
• Case Study 1
• Case Study 2
• Internal Controls
• Tools and Resources
• Summary
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Context
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Department of Conservation
11 Conservancies44 Area Offices1800 – 3200 StaffBudget NZ$390mAssets NZ$5.4b3 Auditors
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What business are they in?
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What business are they in?
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What business are you in?
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Analytics
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Quick Poll
Does your organisation use data analytics in fraud prevention and/or detection?
Yes
No
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Data vs. Information
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Data Analytics
• Collect• Transform• Analyse• Mine• Report
InformedDecisions
Traditional risk based analytics
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Text Analytics
• Pattern• Frequency• Concept• Linkages• Key word
association
Based on what you already know
Non-traditional analytics
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Visual Analytics
• Coordinate mapping
• Relationship• Linkages• Pattern• Proximity
analysis
Non-traditional analytics
Based on what you already know
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Visual Analytics
Non-traditional analytics
https://maps.google.com.fj/locationhistory
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Visual Analytics
Non-traditional analytics
https://maps.google.com.fj/locationhistory
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Case Study 1
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Case Study 1
• Excessive fuel usage noted
• Matched to fuel consumption
• Invoice documentation & approval
• Email connections and correlations
• PCard - purchase expensive & attractive items
• Public auction site link identified
• Handed over to Police
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Case Study 1
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Loc 1 Loc 2 Loc 3 Loc 4 Loc 5 Loc 6 Loc 7 Loc 8 Loc 9 Loc 10 Loc 11 Loc 12 Loc 13
Avg Mthly Fuel Consumption
Avg Mthly Fuel Purchase
Fuel Usage
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Case Study 1Invoices
Invoice documentation• Evidence of delivery• No usage data provided
Invoice approval• User logged as the manager• Approved by site accountant• Circumvented approval limits
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Case Study 1Email
Text Analytics of emails from these individuals identified two strong correlation and several weak
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Case Study 1Email
Motivation Meet budget, favour, problem, committing, creative, concern, not sure, spread, revise, release,
Opportunity Override, write-off, capture cost,correct, appropriate, reserve, misconduct, departing, discount, difficult, fail, critical.
RationalisationIt’s OK, sounds reasonable, I deserve, therefore, find out, get back, find it, figure out, catch, doesn’t make sense.
Keyword
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Case Study 1PCard
PCard• Over 3 yr period – excessive attractive assets• Purchase and sales of vehicles
Auction sites • On sold public assets
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Discussion
Based on the analytics we discussed, what could the auditor have done to enhance/strengthen the internal controls using analytics?
What triggers could have been useful to detect this before it happened?
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• Exceptionally high use of helicopters
• Circumvent delegated authority
• Matched flight plans against Google Earth
• Email pattern and connections
• Fictitious invoices created
• Fictitious vendor set up
• Handed over to Police
Case Study 2
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Case Study 2
Total Heli Use 2012/13
Selected an office where value of flights could not be justified against amount of areal work required
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Case Study 2
Office Loc 1
Within the selected location, one supplier had more business than any other supplier
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Case Study 2
Isolated one individual who procured more helicopter use at this location than anyone else
Circumvented approvals limits
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Case Study 2
Obtained flight log from supplier and plotted route on Google Map
Locations flown had no assets
Asset Location
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Case Study 2
Text analytics of email trails identified two with the highest email traffic and one with suspect name
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Case Study 2
Suspect name turns out to be a fictitious vendorSet up approved by manager
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Discussion
Based on the analytics we discussed, what could the auditor have done to enhance/strengthen the internal controls using analytics?
What triggers could have been useful to detect this before it happened?
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Internal Controls
• Internal controls are only as good as the people who administer them. Many organisations claim to have sound control frameworks but in reality very few do.
• Proactive framework requires proactive people; and lots of thinking to identify “what could” and not “what is”.
• Most people are reactive (human nature). To many organisations, to identify “what could” is seen as a waste of resource (time and money).
• End result – reliance is placed on trust, hope, culture and people.
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Tools and Resources
• Search engines• Public records for addresses and phone
numbers• Digital footprint• Birth, death and marriage registrar• Reverse look ups (phone & addresses)• PPSR (personal property security registrar)
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Tools and Resources
• Google Earth (2D, 3D, Street View and Areal)• Physical address
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Tools and Resources
• Social media (LinkedIn, Facebook, Twitter, etc.)• Online news sites
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Tools and Resources
What do you search for?• Topical searches• Boolean logic• Refine and narrow searches• Acronyms, aliases and AKA’s• Try common misspellings• 5 wives and a husband (5W1H)
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Tools and Resources
How do you search• Known particulars• Known events• Use “AND”, “OR” and “NOT”• Quotation marks• Character strings
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Summary
• Understand your business
• Difference between data and information
• Different types of analytics
• What you can do with data
• Case studies
• Limitations of internal control
• Tools at your disposal to mine data for information
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