Category: Topics in Quantitative Analysis

Bayesian Improved Surname Geocoding (BISG) Race Predictor

The Bayesian Improved Surname Geocoding (BISG), is used by the CFPB to determine race and ethnicity proxies. In recent years the algorithm has been used to determine alleged discrimination at auto finance companies, including an $80 million dollar fine for a well-known bank. This method is far from perfect. For example, someone’s ascribed probabilities can change due to marriage or change of residence.

I created the prototype of a calculator using R/Shinydashboard available at https://pabdndiaye.shinyapps.io/bisg_shiny/ for readers to play with. It takes as inputs: ‘name’ and ‘zip code’ and ascribes the probabilities of that person being of various races and ethnicities using the Bayes rule.

 

Understanding Correlations and Copulas in Finance: An Application in Risk Portfolio Aggregation using R

I am not a fan of articles where the authors use widgets and other unrelatable examples to illustrate complex concepts. Here I will illustrate the use of copulas in finance using the example of risk aggregation to drive through the points. First, though, it is important to briefly explain the risk aggregation problem. There are plenty of texts available on the internet which go in great detail about the inner workings of copulas. My goal is to help the reader develop an intuitive understanding of how copulas are used in finance. Continue reading Understanding Correlations and Copulas in Finance: An Application in Risk Portfolio Aggregation using R

Machine Learning and Credit Risk (part 6) – Multi-class Linear Discriminate Analysis

Linear Discriminate Analysis (LDA) is another method which should be familiar to statisticians and economists.  LDA is a dimensionality reduction technique which has found its use in machine learning because of how well it functions as a classifier. Its primary goal is to project data onto a lower dimensional space. Continue reading Machine Learning and Credit Risk (part 6) – Multi-class Linear Discriminate Analysis

Machine Learning and Credit Risk (part 5) – Neural Networks

Neural networks models are a flexible class of machine learning algorithms which can be used for both supervised as well as unsupervised learning and can approximate discrete or continuous functions.  They are loosely modeled on the functions of human brains and attempt to allow computers to learn in manner similar to humans. Continue reading Machine Learning and Credit Risk (part 5) – Neural Networks