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POST
/
api
/
validate
curl -X POST "https://api.example.com/api/validate" \
  -H "Authorization: Bearer YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "scan_id": "abc123",
    "dataset": "ibm_aml",
    "label_column": "IsLaundering"
  }'

{
  "metrics": {
    "precision": 0.87,
    "recall": 0.92,
    "f1": 0.89,
    "true_positives": 115,
    "false_positives": 17,
    "false_negatives": 10,
    "true_negatives": 858,
    "validated_against": "IBM_AML_IsLaundering",
    "total_labeled": 1000,
    "summary": "Detected 115 true positives with 87% precision and 92% recall"
  },
  "per_rule": [
    {
      "rule_id": "AML_HIGH_RISK_COUNTRY",
      "precision": 0.85,
      "recall": 0.90,
      "f1": 0.87,
      "detected": 45
    },
    {
      "rule_id": "AML_STRUCTURING",
      "precision": 0.89,
      "recall": 0.95,
      "f1": 0.92,
      "detected": 70
    }
  ],
  "summary": "Detected 115 true positives with 87% precision and 92% recall",
  "validated_against": "IBM_AML_IsLaundering",
  "total_labeled": 1000
}
Computes accuracy metrics by comparing detected violations against ground-truth labels from a dataset. Returns precision, recall, F1 score, and per-rule breakdowns to evaluate detection performance.

​
Request Body

​
scan_id
string
required
The unique identifier of the scan to validate
​
dataset
string
required
The dataset name containing ground-truth labels. Supported values: ibm_aml, paysim
​
label_column
string
required
The column name in the dataset containing ground-truth labels.For ibm_aml: use IsLaunderingFor paysim: use isFraud

​
Label Format

Ground-truth labels should be boolean or binary values:
  • true / 1: Indicates a positive case (e.g., fraudulent transaction, money laundering)
  • false / 0: Indicates a negative case (e.g., legitimate transaction)

​
Response

​
metrics
object
Overall validation metrics
​
precision
number
Precision score (TP / (TP + FP))
​
recall
number
Recall score (TP / (TP + FN))
​
f1
number
F1 score (harmonic mean of precision and recall)
​
true_positives
number
Number of correctly detected violations
​
false_positives
number
Number of incorrectly detected violations
​
false_negatives
number
Number of missed violations
​
true_negatives
number
Number of correctly identified non-violations
​
validated_against
string
The ground-truth label column used for validation (e.g., IBM_AML_IsLaundering, PaySim_isFraud)
​
total_labeled
number
Total number of labeled records in the dataset
​
summary
string
Human-readable summary of validation results
​
per_rule
array
Per-rule breakdown of validation metrics
​
rule_id
string
Rule identifier
​
precision
number
Precision for this specific rule
​
recall
number
Recall for this specific rule
​
f1
number
F1 score for this specific rule
​
detected
number
Number of violations detected by this rule
​
summary
string
Human-readable summary of validation results
​
validated_against
string
The ground-truth label column name
​
total_labeled
number
Total number of labeled records

​
Error Responses

​
error
string
Error code: VALIDATION_ERROR, NOT_FOUND, or INTERNAL_ERROR
​
message
string
Human-readable error message
​
details
array
Validation error details (only present for VALIDATION_ERROR)
curl -X POST "https://api.example.com/api/validate" \
  -H "Authorization: Bearer YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "scan_id": "abc123",
    "dataset": "ibm_aml",
    "label_column": "IsLaundering"
  }'

{
  "metrics": {
    "precision": 0.87,
    "recall": 0.92,
    "f1": 0.89,
    "true_positives": 115,
    "false_positives": 17,
    "false_negatives": 10,
    "true_negatives": 858,
    "validated_against": "IBM_AML_IsLaundering",
    "total_labeled": 1000,
    "summary": "Detected 115 true positives with 87% precision and 92% recall"
  },
  "per_rule": [
    {
      "rule_id": "AML_HIGH_RISK_COUNTRY",
      "precision": 0.85,
      "recall": 0.90,
      "f1": 0.87,
      "detected": 45
    },
    {
      "rule_id": "AML_STRUCTURING",
      "precision": 0.89,
      "recall": 0.95,
      "f1": 0.92,
      "detected": 70
    }
  ],
  "summary": "Detected 115 true positives with 87% precision and 92% recall",
  "validated_against": "IBM_AML_IsLaundering",
  "total_labeled": 1000
}

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