{"uid":"cap_3mn7DZpMFikWZsA09lI5A","slug":"expense-processing-iqr-outlier-flags-keyed-dataset-comparison-7c98b041","name":"Expense Processing — IQR Outlier Flags + Keyed Dataset Comparison","description":"Expense processing — IQR outlier flags + Keyed dataset comparison: IQR outlier flags + Keyed dataset comparison for supplied expense processing records (submitted_amount, approved_amount). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 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two statistical reports on up to 200 expense rows: IQR-based outlier detection and a keyed dataset comparison for submitted_amount or approved_amount fields.","exampleAgentPrompt":"Analyze these 150 expense rows for my finance team — flag any IQR outliers in the submitted_amount field and compare this dataset against our prior month's records using the row IDs to spot discrepancies.","exampleUseCases":[{"title":"Monthly expense audit for finance team","prompt":"I have this month's expense records for all our teams — can you run an IQR outlier check on the submitted_amount field and compare it row-by-row against last month's data to see what's changed or looks suspicious?"},{"title":"Detect inflated submitted amounts","prompt":"Here are 80 expense rows from our sales group — flag any submitted_amount values that are statistical outliers using IQR, and cross-check them against the approved_amount dataset so I can see where the gaps are largest."},{"title":"Cross-team expense discrepancy report","prompt":"I've got two expense datasets — one from group-a and one from group-b — can you compare them by row ID and also run IQR outlier detection on the approved_amount field so I know which rows look abnormal?"}],"resultDescription":"Returns two independent reports: (1) an IQR outlier analysis identifying which expense rows have submitted_amount or approved_amount values falling outside the interquartile range, and (2) a keyed dataset comparison report showing row-by-row differences between the primary and secondary supplied datasets, matched by row_id.","failureModes":["Fewer than 1 row submitted — validation error","More than 200 rows submitted — request rejected","Missing required fields (row_id, date, team_group) — schema validation failure","Invalid date format (must be YYYY-MM-DD) — pattern validation error","IQR analysis may be unreliable with very small datasets (fewer than ~10 rows)","Mismatched row_ids between primary and comparison dataset reduce keyed comparison coverage"],"whenToPreferThis":"Choose this endpoint when you need both IQR-based statistical outlier detection and a keyed row comparison in a single call on expense data (submitted_amount, approved_amount), at low cost ($0.01 USDC for both reports). Ideal for batch expense auditing workflows where you have two snapshots of expense data to compare and want to flag anomalies simultaneously. Not suitable if you need live data lookup, predictive modeling, or more than 200 rows per call.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-28T06:47:54.848Z","isFirstParty":false,"canonicalSlug":"expense-processing-iqr-outlier-flags-keyed-dataset-comparison-7c98b041"}