Detecting Financial Crimes: Current State, Limitations, and A Way Forward

Financial Intelligence Units (FIUs) around the world collect data like threshold transaction reports, international fund transfer reports, and suspicious matter/activity reports from Reporting Entities (REs), which include banks, money remitters, casinos, law firms, real-estate companies, and financial companies. They may also get data about entities of interest from partner agencies (PAs) like law-enforcement agencies (LEAs) … More Detecting Financial Crimes: Current State, Limitations, and A Way Forward

In-Database Machine Learning Illustrated

I have just received the excellent news that Apache MADlib, a big data machine learning library for which I was a committer until recently, has graduated to become a top-level Apache project. The basic idea behind MADlib is actually quite interesting and deserves to be more widely known. Massively Parallel Processing (MPP) databases like Greenplum have … More In-Database Machine Learning Illustrated

Setting up a Data Science Practice: Analytics Processes

In this third post on setting up a data science practice, I address some of the analytics processes that need to be in place to maximise value from analytics. After more than two decades of practice and development, there are now well- established data analytics frameworks like the Cross Industry Standard Process for Data Mining. … More Setting up a Data Science Practice: Analytics Processes

Setting up a Data Science Practice: People Dimension

In the previous post, we discussed the key principles of setting up a data science practice. In this post, we’ll discuss the people dimension. One should read the below as suggestions, not prescriptions. There is more than one way to set up a data science practice. Critical to the success of a data science practice are … More Setting up a Data Science Practice: People Dimension

Setting up a Data Science Practice: Fundamental Principles

I have been involved in the setup of several data science practices in both industry and government. Here are a few key principles I use in establishing a data science practice. Principle 1: Building a predictive enterprise is, first and foremost, about building a human infrastructure. Many companies mistakenly believe that analytics is primarily about software … More Setting up a Data Science Practice: Fundamental Principles