Presentation Type
Oral
Student
No
Track
Finance/Insurance Application
Abstract
This presentation will focus first on providing an overview of Channel and the Risk Analytics team that performed this case study. Given that context, we’ll then dive into our approach for building the modeling development data set, techniques and tools used to develop and implement the model into a production environment, and some of the challenges faced upon launch. Then, the presentation will pivot to the data engineering pipeline. During this portion, we will explore the application process and what happens to the data we collect. This will include how we extract & store the data along with how it is integrated into our other systems for decision purposes. We will also talk about how the data is transformed from a raw, sometimes unstructured state, to something more usable by a data science team – and demonstrate how this data was harnessed to help guide model enhancements as key opportunity areas have been identified.
Start Date
2-8-2022 11:00 AM
End Date
2-8-2022 12:00 PM
Included in
Analysis Commons, Applied Statistics Commons, Business Administration, Management, and Operations Commons, Business Analytics Commons, Business Intelligence Commons, Categorical Data Analysis Commons, Data Science Commons, Finance and Financial Management Commons, Numerical Analysis and Scientific Computing Commons, Probability Commons, Statistical Models Commons
Session 5: Equipment Finance Credit Risk Modeling - A Case Study in Creative Model Development & Nimble Data Engineering
Pasque 255
This presentation will focus first on providing an overview of Channel and the Risk Analytics team that performed this case study. Given that context, we’ll then dive into our approach for building the modeling development data set, techniques and tools used to develop and implement the model into a production environment, and some of the challenges faced upon launch. Then, the presentation will pivot to the data engineering pipeline. During this portion, we will explore the application process and what happens to the data we collect. This will include how we extract & store the data along with how it is integrated into our other systems for decision purposes. We will also talk about how the data is transformed from a raw, sometimes unstructured state, to something more usable by a data science team – and demonstrate how this data was harnessed to help guide model enhancements as key opportunity areas have been identified.