A Framework to Detect Waste and Fraud in Health Insurance

A challenge with fraud-detection problems in many cases is the lack of any meaningful collection of labelled data for supervised-learning approaches to work. Two things practitioners do to tackle the problem are statistical profiling, usually via domain-specific business rules, and statistical outlier detection, sometimes augmented with non-trivial models of what constitute “normal” behaviour. There is … More A Framework to Detect Waste and Fraud in Health Insurance

Large-Scale Distributed Analytics: A Research Program

Since starting my part-time appointment as an associate professor at the Australian National University, I have been thinking about spending more time on fundamental research. As Don Knuth counsels, “if you find that you’re spending almost all your time on theory, start turning some attention to practical things; it will improve your theories. If you … More Large-Scale Distributed Analytics: A Research Program

The Learn-R Algorithm

Identify a problem that would benefit from using R Read An Introduction to R by Venables, Smith, and the R Core Team Work on solving the identified problem using R Get access to resources with lots of R code templates for solving different problems (e.g. Handbook of Statistical Analysis using R by Everitt and Hothorn and Data … More The Learn-R Algorithm

Agile Data Science: A Portfolio Approach to Managing An Analytics Team

I recently concluded a two-year stint managing a team of ten highly skilled analytics professionals spread across three different locations. There were of course many challenges but the team over-achieved on just about every measure of success one can imagine. The team’s wins include completing on-time and under-budget a data-matching project that delivers tens of … More Agile Data Science: A Portfolio Approach to Managing An Analytics Team