One Data Scientist’s Primitive Approach to Analyzing the SunTrust – BB&T Merger

Recently when I heard that SunTrust and BB&T merged, I casually wondered about the footprint of the combined bank. Although the number of branches is not necessarily an indicator of the health or performance of a bank, there are benefits to customers of having brick and mortar branches of commercial banks. A few of these include:

  • Readily available ATMs to help in cash withdrawals
  • Customers’ ability to transact large cash withdrawals
  • More robust banking relationships as customers tend to more readily trust banks with branches they can walk into
  • Human contact to answer money-related questions

Quite often, a data scientist’s job is to summarize and communicate information in a clear and concise manner. We’ve all heard the phrase, “a map is worth a thousand words”, and with good reason. Maps are easy to interpret, they are nice to look at and they give us context without having to use too many words. In that same spirit, below we present some maps highlighting the geographic coverage of SunTrust, BB&T and that of the combined entities. A visual approach seems to be a rather pleasing way to allow an audience to rapidly understand the raw location data.

Continue reading One Data Scientist’s Primitive Approach to Analyzing the SunTrust – BB&T Merger

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

Machine Learning and Credit Risk (part 4) – Support vector Machines

Support Vector Machines (SVM) algorithms are some of the best “out-of-box” machine learning tools available. They are not only used for both linear and nonlinear classifications but can also be extended from binary classification to support multi-class classification. Continue reading Machine Learning and Credit Risk (part 4) – Support vector Machines