Hunting Criminals with Hybrid Analytics

Fraud detection is a classic adversarial analytics challenge: As soon as an automated system successfully learns to stop one scheme, fraudsters move on to attack another way. Each scheme requires looking for different signals (i.e. features) to catch; is relatively rare (one in millions for finance or e-commerce); and may take months to investigate a single case (in healthcare or tax, for example) – making quality training data scarce. This talk will cover, via live demo and code walk-through, the key lessons we've learned while building such real-world software systems over the past few years. We'll be looking for fraud signals in public email datasets, using IPython and popular open-source libraries (scikit-learn, statsmodel, nltk, etc.) for data science and Apache Spark as the compute engine for scalable parallel processing. The model is an ensemble using a combination of natural language, graph analysis and time series analysis features, and is re-trained using an automated pipeline to learn from feedback on the fly.