Category: Topics in Machine Learning

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

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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.

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Machine Learning and Credit Risk (part 2) – Credit Cycle Method

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The canonical method to forecasting a credit migration matrix is an econometric model: the one factor approach described in Belkin et al. (1998). This approach suggests that one might consider an approach to condition migration (transition) matrices by creating a systematic component which represents the “credit cycle” that relates the economic condition to the credit quality of a loan portfolio.  The credit cycle can be thought of as the historical pattern of credit rating shared by all borrowers in a sector or economy.Read More »

Machine Learning and Credit Risk (part 1)

Ab N'Diaye4 Comments

Artificial Intelligence (AI) and Data Science continue their progression towards becoming mainstream and ubiquitous. This is a very exciting time for scientists, model developers, programmers, and a lot of other technically inclined professionals. But to be honest it can be confusing and overwhelming at times. We all hear terms like “AI”, “Data Science”, “Big Data”, “Machine Learning”, “Statistical Learning”, “Data Mining”, “Deep Learning”, etc., and it’s often hard to make sense of it all even for those of us who have been writing code to implement statistical models for decades. But it seems these terms are being used among people in every field and every industry. How do remote sensing professionals use data from a satellite to create land cover maps? how do certain streaming services determine what shows or movies to recommend based on your watching habits? How did Cambridge Analytica determine the poor shmucks Donald Trump should focus on? The answers to all these questions lay in machine learning algorithms.  (If interested you can find more information on the differences or definitions of all the terms mentioned above on various discussion threads on social sites like Quora, StackExchange, LinkedIn, and KDNuggets among others.)

This article will be a little more focused on the question: how can we use machine learning in areas where statistics have traditionally been employed in credit risk?

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