Aric LaBarr, PhD

Aric LaBarr, PhD

Associate Professor of Analytics at Institute for Advanced Analytics, NC State University

    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.

    All Sessions by Aric LaBarr, PhD

    Day 2 04/24/2024
    11:00 am - 1:00 pm

    Developing Credit Scoring Models for Banking and Beyond

    <span class="etn-schedule-location"> <span class="firstfocus">Machine Learning</span> </span>

    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.

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