Credit Models and Binning Variables Are Winning and I’m Keeping Score!
Credit Models and Binning Variables Are Winning and I’m Keeping Score!


Classification scorecards are a great way to predict things because the techniques used in the banking industry specializing in interpretability, predictive power, and ease of deployment. The banking industry has long used credit scoring to determine credit risk—the likelihood a particular loan will be paid back. However, the main aspect of credit score modeling is the strategic binning of variables that make up a credit scorecard. This strategic and analytical binning of variables provides benefits to any modeling in any industry that needs interpretable models. These scorecards are a common way of displaying the patterns found in a classification model—typically a logistic regression model, but any classification model will benefit from a scorecard layer. However, to be useful the results of the scorecard must be easy to interpret. The main goal of a credit score and scorecard is to provide a clear and intuitive way of presenting classification model results. This talk will discuss the mathematical underpinnings of conditional inference trees to strategically bin variables and weight of evidence (WOE) values applied to these bins as well as the way to implement these into a variety of classification models. This talk will also cover the implementation of this approach to credit modeling in economically developing countries around the world. These developing countries don't have the credit institutions that countries like the United States have. That leaves these institutions and banks hampered on their ability to make loan decisions. These techniques allowed these institutions to use advanced modeling, but with an interpretable layer on top for easy implementation and data-decisions.


A Teaching Associate Professor at the Institute for Advanced Analytics, Dr. Aric LaBarr is passionate about helping people solve challenges using their data. There he helps design the innovative program to prepare a modern workforce to wisely communicate and handle a data-driven future at the nation's first master of science in the analytics degree program. He teaches courses in predictive modeling, forecasting, simulation, financial analytics, and risk management.

Previously, he was Director and Senior Scientist at Elder Research, where he mentored and lead a team of data scientists and software engineers. As director of the Raleigh, NC office he worked closely with clients and partners to solve problems in the fields of banking, consumer product goods, healthcare, and government.

Dr. LaBarr holds a B.S. in economics, as well as a B.S., M.S., and Ph.D. in statistics — all from NC State University.

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