Agile Data Science: Don’t Let Your Model Die in a Powerpoint Presentation

Most data science projects are doomed to failure before they even start. There are a couple of reasons. The aspiring data scientist and management may be drawn to a sexy problem rather than an important problem. The full range of data required to do a complete analysis may be inaccessible or even non-existent. And even … More Agile Data Science: Don’t Let Your Model Die in a Powerpoint Presentation

Practical Algorithms for Distributed Privacy-Preserving Risk Modelling

In a previous post on the problem of detecting complex financial crimes, I described the following basic technology framework for financial intelligence units (FIUs) and their partner agencies and reporting entities (REs) to engage in collaborative but privacy-preserving and distributed risk modelling using confidential computing technologies. In this post, I describe a few concrete algorithms that … More Practical Algorithms for Distributed Privacy-Preserving Risk Modelling

How to Quickly and Meaningfully Improve the Financial System’s Collective Ability to Detect Crimes

Complex financial crimes are hard to detect primarily because data related to different pieces of the overall puzzle are usually distributed across a network of financial institutions, regulators, and law-enforcement agencies. The problem is also rapidly increasing in complexity because new platforms are emerging all the time that facilitate the transfer of value across a … More How to Quickly and Meaningfully Improve the Financial System’s Collective Ability to Detect Crimes

Extending the Paillier Cryptosystem to Handle Floating Point Numbers

The Paillier Cryptosystem is a partial homomorphic encryption scheme that supports two important operations: addition of two encrypted integers and the multiplication of an encrypted integer by an unencrypted integer. In practice, many applications of Paillier require an extension of the underlying scheme beyond integers to handle floating-point numbers. For example, just about every popular machine learning … More Extending the Paillier Cryptosystem to Handle Floating Point Numbers

The Education of a Data Scientist: On Sands and Other Irritants

I have learned over the years to distinguish between good data scientists and great data scientists in the way they handle the seemingly mundane aspects of data analysis, tasks like loading large but poorly structured datasets, dealing with missing data or poor quality data, finding the right way to interrogate and transform variables to satisfy … More The Education of a Data Scientist: On Sands and Other Irritants

How to Link Millions of Addresses with Ten Lines of Code in Ten Minutes

Solving big hairy problems like detecting complex financial crimes requires solving a series of smaller, mundane but technically non-trivial problems. Performing efficient record linkage on large databases with tens to hundreds of millions of rows of data is one such pesky problem. A few of my colleagues have just made a small dent on the overall … More How to Link Millions of Addresses with Ten Lines of Code in Ten Minutes

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