Buenos Aires Province accounts for most of the fine records we observe in Argentina. The provincial traffic-infraction administrative court system, created by provincial law 13.927, rules on infractions committed on provincial or national routes, motorways and roads within the province, while each partido keeps its own misdemeanour court for urban streets. For a company whose vehicles move through Greater Buenos Aires and the inland routes, that means two layers of authorities, two publication rhythms and a single plate that can accumulate fines in both.

Context and authority

The provincial InfraccionesBA portal allows plate lookups and payment by card or payment slip; under an agreement with the national vehicle registry, payments are also accepted at local registry offices. Amounts are expressed in fixed units (UF) whose value is updated periodically, so the peso amount of the same fine changes over time. The deadline to file a defence is five business days from notification, and joining a voluntary payment plan implies acknowledging the debt.

Limitation periods follow the National Traffic Law 24.449: two years for minor offences and five for serious ones. In practice, a fleet operating in Lanús, Lomas de Zamora, Avellaneda or Ezeiza also receives municipal fines that never appear on the provincial portal. A complete check has to walk both levels with the same discipline.

What the API returns for this jurisdiction

For a given plate, the APINITRO traffic-fines API queries the provincial source (InfraccionesBA) and the municipal misdemeanour courts integrated in the province, and returns each fine with the context needed to decide without going back to the portal:

  • The authority that issued and rules on the fine, with its identifier exactly as published by the source.
  • Infraction date, description or category and, when published, the amount reported in pesos or in UF.
  • Published status, due dates and voluntary-payment availability, without inferring equivalences between authorities.
  • References to the downloadable payment slip or record when the source exposes them.
  • Lookup coverage: which sources answered completely and which remained pending.

What the data shows

Proprietary dataBuenos AiresSeptember 1, 2025 – August 31, 2026
Lookups with at least one open fine
47%
Fines offering voluntary early payment
82%
Median age of fines without a recorded resolution
250 days
Median amount band
ARS 250,000 – ARS 500,000

Mix by infraction category

  1. Speeding60%
  2. Red light and right of way19%
  3. Parking3%
  4. Seat belt, helmet and phone4%
  5. Lanes and manoeuvres12%
  6. Other infractions1%

Monthly distribution of fines

  1. Jan11%
  2. Feb10%
  3. Mar11%
  4. Apr8%
  5. May6%
  6. Jun4%
  7. Jul2%
  8. Aug0%
  9. Sep13%
  10. Oct13%
  11. Nov11%
  12. Dec13%

Share by authority

  1. Avellaneda5%
  2. Bahía Blanca0%
  3. Berisso1%
  4. Ezeiza4%
  5. Florencio Varela1%
  6. Hurlingham1%
  7. Lanús6%
  8. Lomas de Zamora6%
  9. Mar del Plata0%
  10. Provincial (InfraccionesBA)76%
  11. San Miguel0%

Sample: 20.000–50.000 fine records, 50.000+ lookups · See the data methodology

During the period analysed, 47% of plate lookups had at least one fine in the province: the highest share in the country, reflecting both traffic volume and the coverage of the provincial source, which explains most of the observed fines compared with the partido courts.

Fines concentrate on speeding and red-light offences, with a median amount band of ARS 250,000 – ARS 500,000. The median age of fines without a recorded resolution is 250 days, and 82% offered voluntary payment at lookup time: for an operation, that window is the difference between paying with a discount and absorbing the full amount.

The monthly distribution shows more fines in September, October and December; it pays to reinforce checks before the busiest travel seasons.

Case study: fleets that look up plates in Buenos Aires

Proprietary dataFleet cohortSeptember 1, 2025 – August 31, 2026
Fleet lookups with at least one open fine
61%
Fleet accounts in the sample
50
Distinct plates looked up by fleets
2,040

Sample: 20.000–50.000 fine records, 50.000+ lookups · See the data methodology

We define fleet accounts as those that looked up twenty or more distinct plates in the period: 50 accounts that checked 2,040 plates. In the province, 61% of their lookups found at least one fine, above the general average: fleets drive more, and on controlled routes.

The operational pattern repeats itself: checks were done by hand on the provincial portal when a unit was returned or before a contract was renewed, and municipal fines surfaced late, after the voluntary-payment window had closed. Integrating the lookup into each vehicle onboarding and each contract closure turns a reactive chore into a scheduled control, with the authority and the due date of every fine on the operator’s screen.

No figure in this case identifies a company: they are cohort aggregates, rounded according to the data methodology.

How to integrate

A lookup is a POST with the plate and, optionally, the jurisdictions to include; the initial response accepts the job and the result arrives through status polling or a webhook, because official sources answer in seconds or minutes.

JSONIllustrative example, not a contract.
{
  "plate": "ABC123",
  "jurisdictions": [
    "AR-B"
  ]
}

Persist the idempotency key before sending and treat each plate as one operation: the guide on asynchronous APIs explains how to retry without duplicating work, and the API page details access.

Limits and considerations

  • Partido coverage grows by integration; a partido not listed on the API page is not queried even if it has a misdemeanour court.
  • UF amounts are converted at the value current at the source; the final amount is set by the authority at payment time.
  • A source that does not respond is reported as pending, never as an absence of infractions.
  • Our data comes from user lookups, not from a registry: the share of fines is biased towards suspicious plates.
  • The API neither pays fines nor files defences; it delivers information to decide.

Sources and further reading