TopN AnalyticsFraud Detection for Undisclosed Fleet Trips
A large EV fleet operator running electric vehicles wanted to identify vehicle trips that were not being properly reported or recorded, helping reduce revenue leakage and strengthen operational control across its fleet.
Where things stood
Drivers are contractually required to use their assigned EVs exclusively for trips on one registered ride-hailing platform, but the fleet operator's only visibility into usage came from that platform's own trip records.
Any usage outside the registered platform was functionally invisible, creating both a revenue leakage risk and an accountability gap.
The core difficulty: detecting something that, by design, has no record in the system being monitored. The operator needed an independent signal of actual vehicle usage that didn't depend on the ride-hailing platform's own data.
What we built
Vehicle Telematics (BigQuery), Ride-Hailing Trip Log (CSV), and Driver Check-in/out Logs (PostgreSQL) fed a Python/DuckDB join and feature-engineering layer.
A logistic regression model (scikit-learn, SMOTE-balanced) predicted whether a vehicle was 'on a trip' using only its own telematics — speed and the rate of change in battery state-of-charge (SoC) — with no dependency on the ride-hailing platform's records.
Logistic regression was selected over Random Forest and XGBoost alternatives for interpretability, with a decision threshold tuned to favor recall.
Predicted vs. platform-confirmed kilometers were reconciled per vehicle and driver into reporting outputs.
How it works
TopN Analytics built a supervised machine learning pipeline that compares what a vehicle's own telematics says happened against what the ride-hailing platform recorded, where telematics indicates driving activity but no matching trip exists in the platform's records, that gap becomes a flag for investigation.
The pipeline integrates three distinct data sources: onboard vehicle telematics, the ride-hailing platform's trip log, and driver check-in/check-out records that attribute a given vehicle session to a specific driver.
What we delivered and learned
The ML model provided mechanism for trip reconciliation and anomaly detection framework helped identify suspicious drivers.
What it's built on
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