I will build a fraud and anomaly detection model for your data

Hidden fraud, outliers, or unusual patterns in your data can cost real money if they go unnoticed. I'll build a custom anomaly detection model to flag suspicious transactions, records, or events in your dataset backed by real evaluation metrics, not vague claims.

What you get:

Data cleaning & preprocessing of your dataset

Anomaly detection using statistical methods or deep learning (autoencoder-based reconstruction error)

Flagged records report with confidence/anomaly scores

Clear visualizations of anomaly patterns

Optional: full deployment pipeline with source code

I've applied this exact approach on real transactional datasets (credit card-style fraud data), using autoencoder-based reconstruction error to catch patterns simple rule-based systems miss.

Best suited for: transaction/tabular numeric data financial transactions, insurance claims, sensor logs, user activity data.

Send me your dataset (CSV/Excel) and I'll get started right away. Message me first if you're unsure which package fits your data size.