SCTR7: Data Science and Analytics

As many traditional industrial age business functions become subject to disintermediation by automation and expert analytics systems, new industries and value propositions will need to be framed by an emerging generation of leaders.

Recent collected posts and links to fraud detection and mitigation items: Network analytics for fraud detection & mitigation (video demostration) Part 1 video demo Part 2 video demo Semantics analytics lecture (Erasmus RSM) Demonstration of semantic analytics (video demonstration) Blog post Excuse me, do you speak fraud? Blog post Network analytics for fraud detection Blog post Network analytics: more than […]

In the mid-1980’s I worked as an information analyst / data manager in a police station in a small town in New England (northeast U.S.).  It was a strange but exciting experience for a young man, chosen simply as I was identified as the local ‘computer geek’ at the town high school (a small and […]

Excuse me, do you speak fraud? Network graph analysis for fraud detection and mitigation by Scott Mongeau Executive summary Network analysis offers a new set of techniques to tackle the persistent and growing problem of complex fraud. Network analysis supplements traditional techniques by providing a mechanism to bridge investigative and analytics methods. Beyond base visualization, […]

Network analysis is a rapidly growing analytics domain propelled by the explosion of interest in social networking. The methods rest upon much older foundations in the realms of statistics and social science. Euler’s graph theory was proposed in the early 18th century and Moreno established the foundations for social network analysis (SNA) in the 1930’s. Want […]

As an analytics professional, it may perhaps be no surprise that I believe in a world which is susceptible to analysis – a world which yields to scientific inquiry. Further, I believe that if we invest effort, if we exert what Kahneman would call our System 2, we can improve decisions and overcome our tendency […]

The application of network analysis to the growing challenge of fraud and financial crime is a fast emerging advanced data analytics frontier. As any good fraud investigation knows, fraud and financial crime are as much deep social phenomenon as aspects of financial transactions gone awry. Thus the application of social network analysis is able to […]

A link to a good article proposing that our minds evolved to see patterns and significance, predisposing us to magical and superstitious beliefs: http://theconversation.com/caveman-instincts-may-explain-our-belief-in-gods-and-ghosts-26945 This supports the notion that a major challenge in data science is overcoming our inbuilt behavioral predispositions, for instance the tendency to overfit data samples or to conflate correlation with causation. […]

Recent lecture ‘Introduction to Analytics’ given to Erasmus Rotterdam School of Management MBA and MIS students: Summary overview https://www.youtube.com/watch?v=KmG7FJhms2Q Part 1 of 4: https://www.youtube.com/watch?v=KFYYDrEV4wI Part 2 of 4: https://www.youtube.com/watch?v=KFYYDrEV4wI Part 3 of 4: https://www.youtube.com/watch?v=UPsJx427rKE Part 4 of 4: https://www.youtube.com/watch?v=-1yxe0XjLWg

MODEL RISK: A central implication of the application of advanced analytics in the finance industry is that models, and model-based decision making, is becoming increasingly complex. There are growing risks for financial institutions that have failed to define and adopt formal approaches to model validation. The Deloitte ‘Global Model Practice Survey’ provides insights into financial […]