# Expense Processing — IQR Outlier Flags + Keyed Dataset Comparison

> Expense Processing — IQR Outlier Flags + Keyed Dataset Comparison is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-28).

Runs 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.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-operations-07-179?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-28
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/expense-processing-iqr-outlier-flags-keyed-dataset-comparison-7c98b041
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_3mn7DZpMFikWZsA09lI5A

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability expense-processing-iqr-outlier-flags-keyed-dataset-comparison-7c98b041 -d '<json body>'
```

Example prompt: 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.

## When to prefer this

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.

## Known failure modes

- 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

## How this service works

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 rows.

## Output

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.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "rows": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "team_group",
     "date",
     "submitted_amount",
     "approved_amount"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "team_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     },
     "approved_amount": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "submitted_amount": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "team_group": "group-a",
     "approved_amount": 1,
     "submitted_amount": 10
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "team_group": "group-b",
     "approved_amount": 2,
     "submitted_amount": 20
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "team_group": "group-a",
     "approved_amount": 3,
     "submitted_amount": 15
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "team_group": "group-b",
     "approved_amount": 4,
     "submitted_amount": 30
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "submitted_amount",
    "approved_amount"
   ],
   "type": "string",
   "default": "submitted_amount"
  },
  "other": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "team_group",
     "date",
     "submitted_amount",
     "approved_amount"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "team_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     },
     "approved_amount": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "submitted_amount": {
      "type": [
       "num
… (truncated)
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/expense-processing-iqr-outlier-flags-keyed-dataset-comparison-7c98b041/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from market.datapackvibe.com](https://www.zero.xyz/host/market.datapackvibe.com/llms.txt)