Detect serial defects in fleet data before a recall

Car manufacturers analyse vehicle fleet data to detect serial defects before a recall

Needs setupAI Act: no extra obligationsFinding anomalies & fraudAutomotive

Fits: Research & development · Production, engineering & maintenance

Clusters of fault codes and workshop cases in certain production months, supplier batches or software versions stand out weeks earlier. You can make targeted fixes instead of a broad recall later.

Is this for you?

  • Vehicles send diagnostic data, or warranty and workshop cases are available in structured form with the chassis number.
  • You can link fault data with production month, plant, software version and supplier parts.
  • Quality assurance and product safety currently decide mainly on the basis of warranty costs and individual reports.
First step today

Get an aggregated table from the quality department: reports per fault code and production month for one model series, plus the number of vehicles in the field per production month, without chassis numbers. Use the template below to check whether individual production months stand out.

How to go about it

  1. Define what counts as an anomaly in the field
  2. Link diagnostic, warranty and production data
  3. Set up cluster detection
  4. Have hits checked by experts
  5. Decide on corrective action
  6. Adapt rules to new models and software versions

Templates to copy

Try it now
in the training
Analyse
in the training
Plan the rollout
in the training
Included in the training

The complete guide

  • 6 steps explaining exactly what to do
  • 3 proven templates to copy into your own AI
  • Safe use: data, checking, deciding, informing
  • Pitfalls, effort and how to recognise success

With the training you get all 430 guides for as long as your access runs.

See the trainingOne-off per person, no subscription

Same approach, other use cases

12 more use cases follow the pattern “Finding anomalies & fraud”.

Check claim notifications for signs of fraudCheck service charge statements for errorsDetect cheaters by their gameplay behaviourDetect market manipulation on trading platformsDetect money laundering patterns in account activityDetect phishing emails and suspicious sign-insFind duplicate and suspicious incoming invoicesIntercept fraudulent orders and card paymentsReview client bookkeeping for audit risks in advanceSelect tax cases for review on a risk basisStop fraudulent calls and SIM swap fraudTrack down electricity theft and tampered meters
‹ PreviousSee all use casesNext ›

AT-09 · Legal status 08.10.2026