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Insights On Demand September 12 th, 2013. Agenda Transaction Monitoring Successes What’s Holding It Back Insights On Demand Insights for T&E.

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Presentation on theme: "Insights On Demand September 12 th, 2013. Agenda Transaction Monitoring Successes What’s Holding It Back Insights On Demand Insights for T&E."— Presentation transcript:

1 Insights On Demand September 12 th, 2013

2 Agenda Transaction Monitoring Successes What’s Holding It Back Insights On Demand Insights for T&E

3 Delivering Insights Globally Oversight Clients: Analyzing $1.85 Trillion+ Transactions

4 Oversight Architecture Extractor Acquires data Any data source “Light touch” Set up in hours Doesn’t interfere with IT Mapper Maps data into normalized database Continuously updated data warehouse All client data similar, allowing for reuse of analytics Collaborative Reasoning Engine (CoRE) Human-like analysis Predictive logic (e.g., fuzzy logic, pattern recognition, statistical analysis) Simplified definition of complex analysis Continuous application of analytics Workbench and Dashboard Workflow and reporting Present “indicators” Efficient resolution, usually seconds Accurate and immediate reporting SAP Concur JDE PEP Lists Other Act Analyze Map Extract Oversight Analytics

5 Evidential Reasoning Drive Accuracy Vendor Invoice Compliant Non-compliant Conclusion Exception Created High Risk Invoice Check Remit-to Country ≠ Bank Suspicious Vendor Type High Risk GL Account Payment Frequency Round Dollar Amount Dow Jones Anti- corruption Suspicious Keyword World-Check OFAC SDNCPI High Risk

6 Continuous Monitoring through the Lens of a Maturity Model Classic Continuous Monitoring Analysis provided by Consultants

7 Engaging a Consultant 1.Select appropriate expertise 2.Provide the requested data 3.Interviews to understand the nuances of your situation 4.Initial analysis 5.Review results 6.Issue final report

8 Oversight Competitive Landscape Business Intelligence Consulting Data Analytics Tableau Qlik Business Obj Cognos Birst Good Data Yellow Fin Pentaho IBM Accenture E&Y PwC KPMG Deloitte PRGX Boutique SPSS SAS MapR 1010 Data Alteryx MS Excel MS Access Insights On Demand IBM Analytics Google Analytics SFDC Analytics ACL FirstStrike 8

9 Getting Started with Insights On Demand Select an Insight & click the Get the Insight button T&EP-CardP2P 1 2 Upload data to the Insights on Demand secure web site 3 Tailor the analysis with a 15 minute survey

10 Getting Started with Insights On Demand 4 Receive Your Insights Assessment Report Executive DashboardAnalyst Workbench

11 Insights On Demand Basics Analytics specific to defined objectives Solutions focused on needs of Business Managers Customer Control – Tailoring to their business needs – Teaching the system their business norms – Using at a frequency that makes sense to their business Pricing based on Number of Records and Frequency Discounts related to Volume, Frequency, and Number of Insights No Implementation costs 11

12 Bespoke Results – Off the Rack $ Abstract the common analytic elements – Statistics – Similarity – Normal vs. Unusual Survey for the Client specifics – Risky merchants – Policy elements

13 Business Users Understand Results Analytics LEARN thru this input

14 Insights – Three Examples from T&E T&E Fraud Risk T&E Policy Misuse T&E Duplicate Submissions 14

15 T&E Fraud Risk Identifies potential fraud or abuse Events are defined as egregious violations of the corporate T&E policy – Purchases at inappropriate venues or related to cruise, spa, liquor, online, jewelry etc – Timing and amount of such purchases are also taken into account to precisely identify these violations – Misclassifications, keyword match, weekend and holiday purchases Key Insights – Detect instances of high dollar suspicious purchases – Identify egregious offenders – Visibility into high risk employees’ transactions – Visibility into inappropriate spend categories by merchant category codes and merchants – Benchmark your internal fraud risk over time 15

16 Fraud Risk Example 16

17 T&E Policy Misuse Identifies policy violators and patterns of misuse Policy violators are detected based on behavioral patterns – Circumvention of expense receipt policies – Lodging and meal outlier expenses – Excessive out of pocket spend – Retail purchases at discount stores, pharmacies, grocery stores and other such venues Key Insights – Identify frequent violators of policy – Visibility into consistent violators’ transactions – Visibility into Merchants and Merchant Category Codes with patterns of out of policy spend – Benchmark your internal cardholder misuse over time. 17

18 Policy Misuse: Circumvent receipt limit 18

19 T&E Duplicate Submissions Identifies the same purchase expensed more than once. – Duplicates may appear on the same expense report or across expense reports. Key Insights – Identify employees who frequently submit duplicate expenses. – Find individual duplicate recovery opportunities. – Understand high risk expense report categories for duplicate submissions. – Benchmark duplicate submissions in your organization against industry norms. – Benchmark duplicate submissions internal to your organization over time. 19

20 Summary of Findings Insight Acme Exception Rate Oversight’s Industry Exception Rate Potential Impact (USD) Conservative Annual Savings Potential Annual Savings (USD) Fraud Risk1.3%1% to 3%$5,855,4022% to 5%$117,108 to $292,770 Policy Misuse4%2% to 5%$23,735,0771% to 3%$237,351 to $712,052 Duplicate Submissions0.2%0% to 1%$149,0335% to 10%$7,452 to $14,903 Total annual improvement in spend estimate (conservative)$361,911 to $1,019,725 Acme can expect a minimum annual savings in spend of $361,911, ranging as high as $1,019,725 These savings represent a return on investment (ROI) for Acme starting at 9x 20

21 Influencing Behavior

22 We Deliver the Answers that are In Your Data - Just Try It

23 Fraud Risk Example 23

24 Fraud Risk Example 24

25 Policy Misuse: Excessive out-of-pocket 25

26 Policy Misuse: Suspicious pattern 26

27 Policy Misuse: Meal outlier 27

28 Policy Misuse: Lodging outlier 28

29 Duplicate Submission Example 29

30 Duplicate Submission Example 30


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