SimBox Fraud Detection

STEP / 01

Brief

SimBox fraud reroutes international calls through grey-market hardware, costing operators serious revenue. The detector has to find a moving target in a wildly imbalanced stream.

STEP / 02

Approach

Build a feature space the model can actually learn from.

• 01Exploratory analysis to surface call-pattern fingerprints✦
• 02Engineered features around call duration, IMEI churn, geo-rotation✦
• 03Imbalanced-class strategies (resampling, class-weighting)✦
• 04Metaheuristic search over hyperparameters and feature subsets✦

STEP / 03

Model

Classic supervised classifiers tuned by metaheuristic optimisation. The win was a tighter false-positive rate without giving up recall on a constantly evolving fraud pattern.

STEP / 04

Friction points

Why this was not a textbook problem.

• 01Severe class imbalance with drifting prevalence✦
• 02Adversarial fraud patterns that mutate week-over-week✦
• 03Operator-side cost asymmetry between FP and FN✦

STEP / 05

Outcome

Improved detection rate with materially fewer false alarms — a model robust enough to ride drift without retraining every week.