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Using Analytics to Audit T&E
- 1. USING ANALYTICS TO AUDIT T&E Manuel Coello, Senior Director Data Analytics
- 2. Quality without results is pointless. Results without quality is boring. Johan Cruyff
- 4. Summaries Identify patterns & relationships… Data overview Get some perspective Sankey Diagram Looking at the flows of major transactions Outlier Detection Anomalies within the data Text Analytics Going beyond numbers Predictive Assessing the likelihood Risk Scoring Single number showing the risk Unsupervised Findings those hidden patterns Risk Monitoring Risk at the entity level
- 6. Summaries Identify patterns & relationships… Data overview Get some perspective Sankey Diagram Looking at the flows of major transactions Outlier Detection Anomalies within the data Text Analytics Going beyond numbers Predictive Assessing the likelihood Risk Scoring Single number showing the risk Unsupervised Findings those hidden patterns Risk Monitoring Risk at the entity level
- 8. Summaries Identify patterns & relationships… Data overview Get some perspective Sankey Diagram Looking at the flows of major transactions Outlier Detection Anomalies within the data Text Analytics Going beyond numbers Predictive Assessing the likelihood Risk Scoring Single number showing the risk Unsupervised Findings those hidden patterns Risk Monitoring Risk at the entity level
- 10. SANKEY DIAGRAM
- 11. Summaries Identify patterns & relationships… Data overview Get some perspective Sankey Diagram Looking at the flows of major transactions Outlier Detection Anomalies within the data Text Analytics Going beyond numbers Predictive Assessing the likelihood Risk Scoring Single number showing the risk Unsupervised Findings those hidden patterns Risk Monitoring Risk at the entity level
- 12. TEXT ANALYTICS
- 13. TEXT ANALYTICS
- 14. THERE IS NO TRANSFORMATION UNLESS EVERYONE BENEFITS… • Intro / instructions • Dropdown with your name • Select file • Enter Employee ID of the executive you are auditing Introduction ~<1 min • Drop-down of the fields in the file • Selects 3 key fields for analytics • Other 5 generic fields Mapping ~4 min • Validate data fields (pass/fail) • % errors Data Validation 1 min later • Link to the dashboard Email Link 5-10 min later Dashboard 4 worksheets CREATING ELI
- 15. Summaries Identify patterns & relationships… Data overview Get some perspective Sankey Diagram Looking at the flows of major transactions Outlier Detection Anomalies within the data Text Analytics Going beyond numbers Predictive Assessing the likelihood Risk Scoring Single number showing the risk Unsupervised Findings those hidden patterns Risk Monitoring Risk at the entity level
- 16. Z-score Amount Avg Amount Std Deviation Amount Weightage 1,000 800 720 0.28 450 500 452 (0.11) 58 250 240 (0.80) 37 40 50 (0.06) 200 150 50 1.00 RISK SCORING Quantitative Calculation Quantitative Risk Weightage Employee Pass/Fail Test Weight (1-10) Pass/Fail Test Weight (1-10) Pass/Fail Test Weight (1-10) Score 1 Yes 9 Yes 3 Yes 6 18 2 No 0 Yes 3 Yes 6 9 3 Yes 9 No 0 Yes 6 15 4 Yes 9 Yes 3 No 0 12 5 No 0 No 0 Yes 6 6 Test 1 Test 3 Test 3 Qualitative Calculation Org Level Score Category Name Score Business Activity Score Qualitative Risk Weightage GOV Leadership 6 Dinner 9 Agency/Broker Activity 8 23 NA National Accts Ops 7 Entertainment 9 Sales/Prospect 8 24 TER Territories Admin 4 Projects 7 External Meeting 7 18 SPC Specialty Businesses 8 Lunch 7 Internal Meeting 5 20 FIN Internal Audit 5 Travel Transaction Fee 5 Travel-Training/Education 5 15 FORMULA: Risk Score % = ((Quantitative Risk Weightage * ((Amount – Avg Category Amt) / Std dev Category Amt) + Qualitative Risk Weighted) / Max Score RiskScore Calculation Employee Quantitative Risk Weightage Amount Weightage Qualitative Risk Weightage Risk Max RiskScore Final Risk 1 18 0.28 23 28 250 11% 2 9 (0.11) 24 23 250 9% 3 15 (0.80) 18 6 250 2% 4 12 (0.06) 20 19 250 8% 5 6 1.00 15 21 250 8%
- 17. Summaries Identify patterns & relationships… Data overview Get some perspective Sankey Diagram Looking at the flows of major transactions Outlier Detection Anomalies within the data Text Analytics Going beyond numbers Predictive Assessing the likelihood Risk Scoring Single number showing the risk Unsupervised Findings those hidden patterns Risk Monitoring Risk at the entity level
- 18. RISK MONITORING
- 19. RISK MONITORING
- 20. Summaries Identify patterns & relationships… Data overview Get some perspective Sankey Diagram Looking at the flows of major transactions Outlier Detection Anomalies within the data Text Analytics Going beyond numbers Predictive Assessing the likelihood Risk Scoring Single number showing the risk Unsupervised Findings those hidden patterns Risk Monitoring Risk at the entity level
- 21. UNSUPERVISED
- 23. UNSUPERVISED - KAMILA Kay-means for MIxed LArge data sets Handles continuous and categorical variables Equitably balances both variable types Scalable over large data sets Can be applied over a diverse variety of data sets Can calculate optimal number of clusters Unsupervised technique Combines the popular k-means algorithm and Gaussian-multinomial mixture models Assumes elliptical clusters Pseudocode ResultsCluster Determination
- 24. Summaries Identify patterns & relationships… Data overview Get some perspective Sankey Diagram Looking at the flows of major transactions Outlier Detection Anomalies within the data Text Analytics Going beyond numbers Predictive Assessing the likelihood Risk Scoring Single number showing the risk Unsupervised Findings those hidden patterns Risk Monitoring Risk at the entity level
- 25. Errors, Waste & Abuse Unusual patterns… Excessive spend as per peer group 3x standard deviation as per job family & career band Excessive mileage Potential fictitious mileage Excessive cash transactions below receipt limits Consistent claims below the thresholds Top Transactions Largest transactions Non-T&E Expenses Employees buying AP items or services through T&E Even dollar amount Unusual transactions High Avg spend by category per person vs peer group Excessive spend by category Expenses near or beyond termination date Conflicting expenses
- 26. Fraud Deter it, prevent it & detect it… Suspicious MCCs Leveraging credit cards classifications False meal submission Submitting a meal in cash while being an attendee in another expense Excessive meals near office Using point-to-point distant calculation Excessive spending with same attendee Too much spend from various parties on same business guest Duplicate Payments Claim submissions in cash and credit card Excessive spending without attendee details Blank or ambiguous attendees Excessive frequency in same restaurant High number of meals in same restaurant Unexpected combination expenses Conflicting expenses
- 27. Compliance Controls matter more than ever… Out of Policy reimbursements Testing key aspects of the policy Unapproved Govt. official Expenses Submitting a meal in cash while being an attendee in another expense FCPA risk indicators Calculates Split Transactions Too much spend from various parties on same business guest Use of unapproved travel agency Booking trips within the guidance Unauthorized use of gift card providers Blank or ambiguous attendees Expense Approval First line of defense Not using Corporate Card Conflicting expenses
- 29. THANK YOU