Developing Credit Scoring Models for Banking and Beyond


Classification scorecards are a great way to predict outcomes because the techniques used in the banking industry specialize 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 machine learning 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 training will help the audience work through how to build successful credit scoring models in both R and Python. It will also teach the audience to layer the interpretable scorecard on top of these models for ease of implementation, interpretation, and decision making. After this training, the audience will have the knowledge to be able to build more complete models that are ready to be deployed and used for better decisions by executives.

Session Outline:

1 – Introduction to Credit Scoring / Scorecards:
• Familiarize yourself with the concepts of credit modeling and how to read and use a scorecard.
• Understand the data structure that we typically work with in a credit modeling world.
• Explore the data and variables used in the training with R / Python.

2 – Strategically Binning Variables:
• Compare the two commom strategies of pre-binning and combining vs. decision trees for binning your predictor variables.
• Calculate weight of evidence and information value to see which variables are best used for the modeling with R / Python.

3 – Building the Model and Scorecard:
• Using the binned variables from the previous section, build the underlying credit scoring model with R / Python.
• Apply the scorecard framework on top of the credit scoring model.
• Evaluate the model’s performance using common metrics like AUC and KS statistic.

4 – Implementation / Deployment
• Consider how best to deploy these models when built.
• Talk about further considerations like reject inference.

We will use R and/or Python during the session. A website will be available for the attendees that will have code in both.

Background Knowledge:

1- Familiarization with R and/or Python.
2- Basic modeling knowledge. For example, what is logistic regression, how do we evaluate a binary classification model.


A Teaching Associate Professor in 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 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 led 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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