It’s a place to share what I learned, and I experienced
Building Production Fraud Detection Models, Part 2: Handling Imbalanced Data & Modeling
Background In the last article, I explained how to define a measurable goal and prepare the data. Building a fraud detection model brings its own challenges. Fraudulent transactions make up a tiny minority. Although fraud rates vary across payment services, they are often well below 1%. For example, EBA’s 2025 Report on Payment Fraud, pp. 12 and 15 reports that, in 2024, approximately 0.015% of card transactions and 0.011% of e-money transactions were fraudulent. Class imbalance is the first challenge. Transaction volume adds another: payment services commonly handle over one billion transactions per year. ...
Building Production Fraud Detection Models, Part 1: Goals, Metrics, and Data Preparation
Background Fraud detection modeling is often considered a classic binary classification problem. In practice, however, even a classifier with 99% recall or 99% PR-AUC can still be useless. An extremely imbalanced dataset can cause a classifier to produce a huge number of false positives, which will reduce profits from transaction fees. In this article, I will discuss two major topics that are rarely discussed: How to define a proper goal and a measurable metric Data preparation In the next article, I will cover training practices: ...
Re-Learn PCA in a Visual Implementation
Background Recently, I have been thinking about how to present an example that is both easy to understand and practical. So I decided to write an article to not only implement PCA but also show how it works. Principle Component Analysis (PCA) Principle Component Analysis (PCA) is a powerful technique for multiple variables analysis. PCA reduces the dimensionality of the data while preserving as much variance as possible. It is usually applied to: ...
About This Blog
This is a blog sharing what I learned, and my experience from my work. It’s mainly related to data science, software engineering.