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Application 2: Misstatement detection Problem: Given network and noisy domain knowledge about suspicious nodes (flags), which nodes are most risky? Cash.

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Presentation on theme: "Application 2: Misstatement detection Problem: Given network and noisy domain knowledge about suspicious nodes (flags), which nodes are most risky? Cash."— Presentation transcript:

1 Application 2: Misstatement detection Problem: Given network and noisy domain knowledge about suspicious nodes (flags), which nodes are most risky? Cash Money from A/P to purchase inventory Transfer from A/R, report revenue Accounts Payable Inventory Bad Debt Non-Trade A/R Accounts Receivable Revenue Orange Cty Revenue Los Angeles Revenue San Diego Revenue San Francisco Revenue Other 1

2 Application 2: Misstatement detection Problem: Given network and noisy domain knowledge about suspicious nodes (flags), which nodes are most risky? Cash Accounts Payable Inventory Bad Debt Non-Trade A/R Accounts Receivable Revenue Orange Cty Revenue Los Angeles Revenue San Diego Revenue San Francisco Revenue Other Many late postings Round-dollar entries Large number of returns Many entries reversed late in period 2

3 Application 2: Misstatement detection 3 Solution: Social Network Analytic Risk Evaluation – Assume homophily between nodes (“guilt by association”) – Use belief propagation (message passing) – Upon convergence, determine end risk scores. – Details: See Ch. 9.3.

4 Application 2: Misstatement detection Nodes in proximity of many flags will be marked as risky, nodes flagged in isolation will not. Cash Accounts Payable Inventory Bad Debt Non-Trade A/R Accounts Receivable Revenue Orange Cty Revenue Los Angeles Revenue San Diego Revenue San Francisco Revenue Other 4

5 Application 2: Misstatement detection Nodes in proximity of many flags will be marked as risky, nodes flagged in isolation will not. Cash Accounts Payable Inventory Bad Debt Non-Trade A/R Accounts Receivable Revenue Orange Cty Revenue Los Angeles Revenue San Diego Revenue San Francisco Revenue Other Focus on staff posting to A/R from headquarters Ignore A/P, no corroborating evidence 5

6 Application 2: Misstatement detection 6 Accurate- up to 6.5 lift Flexible- Can be applied to other domains Scalable- Linear time Robust- Works on large range of parameters False positive rate True positive rate Results for accounts data (ROC Curve) Ideal SNARE Baseline (flags only)


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