Match rate monitor
This recipe shows how to monitor the reconciliation match rate over time with trend analysis and rule-level breakdown. Match rate is the key KPI for reconciliation accuracy and directly impacts operational efficiency.
Endpoint
GET /v1/reconciliations/match-rate
Returns a time-series summary of match rates with the chosen granularity and dimension.
Prerequisites
- A valid
tenantIdand bearer JWT. - The user must have at least one of:
tenant_admin,tenant_analyst,tenant_lead.
Request
curl -X GET "https://api.falconveritas.com/v1/reconciliations/match-rate?fromDate=2026-08-01&toDate=2026-08-22&granularity=daily&dimension=rule" \
-H "Authorization: Bearer $FVI_JWT" \
-H "X-Tenant-Id: $FVI_TENANT_ID" \
-H "Content-Type: application/json"
Response
{
"granularity": "daily",
"dimension": "rule",
"from_date": "2026-08-01",
"to_date": "2026-08-22",
"overall_match_rate": 0.984,
"series": [
{
"date": "2026-08-01",
"rule_id": "rule-001",
"rule_name": "Bank-to-GL cash match",
"total_count": 4523,
"auto_matched_count": 4455,
"analyst_review_count": 52,
"exception_count": 16,
"match_rate": 0.985
}
],
"by_rule": [
{
"rule_id": "rule-001",
"rule_name": "Bank-to-GL cash match",
"match_rate": 0.985,
"trend_7d": 0.001
},
{
"rule_id": "rule-002",
"rule_name": "Crypto on-ramp match",
"match_rate": 0.892,
"trend_7d": -0.012
}
],
"alerts": [
{
"rule_id": "rule-002",
"alert_type": "match_rate_decline",
"current_rate": 0.892,
"previous_rate": 0.904,
"deviation": -0.012,
"recommendation": "Review rule-002 thresholds; consider tightening tolerance"
}
]
}
Dimensions
| Dimension | Description |
|---|---|
rule | Break down by reconciliation rule |
account | Break down by account |
source | Break down by ingestion source |
counterparty | Break down by counterparty |
currency | Break down by transaction currency |
Alert types
| Alert type | Description |
|---|---|
match_rate_decline | Match rate dropped below the rolling 7-day average by more than 2% |
match_rate_low | Match rate below the configured threshold (default 0.85) |
rule_outlier | One rule's match rate deviates significantly from the others |
Use cases
- Operations monitoring — track match rate as the primary reconciliation KPI.
- Rule tuning — identify rules that need threshold adjustments.
- Anomaly investigation — investigate sudden drops in match rate.
- Vendor benchmarking — compare match rates across ingestion sources.
Code samples
TypeScript
import { FVI } from '@falconveritas/sdk';
const fvi = new FVI({
baseUrl: 'https://api.falconveritas.com',
bearer: process.env.FVI_JWT!,
tenantId: process.env.FVI_TENANT_ID!,
});
const rate = await fvi.reconciliations.matchRate({
fromDate: '2026-08-01',
toDate: '2026-08-22',
granularity: 'daily',
dimension: 'rule',
});
console.log(`Overall match rate: ${(rate.overall_match_rate * 100).toFixed(1)}%`);
for (const alert of rate.alerts ?? []) {
console.log(`Alert: ${alert.alert_type} for ${alert.rule_id} — ${alert.recommendation}`);
}
Python
from falconveritas import FVI
client = FVI(
bearer=os.environ["FVI_JWT"],
tenant_id=os.environ["FVI_TENANT_ID"],
)
rate = client.reconciliations.match_rate(
from_date="2026-08-01",
to_date="2026-08-22",
granularity="daily",
dimension="rule",
)
print(f"Overall match rate: {rate.overall_match_rate * 100:.1f}%")
for alert in rate.alerts or []:
print(f"Alert: {alert.alert_type} for {alert.rule_id} — {alert.recommendation}")
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Last updated: 2026-08-22 · Page owner: Documentation · Source: src/lib/api/reconciliations.ts.