Abstract
The use of machine learning algorithms to model user behavior and drive business decisions has become increasingly commonplace, specifically providing intelligent recommendations to automated decision making. This has led to an increase in the use of customers personal data to analyze customer behavior and predict their interests in a companys products. Increased use of this customer personal data can lead to better models but also to the potential of customer data being leaked, reverse engineered, and mishandled. In this paper, we assess differential privacy as a solution to address these privacy problems by building privacy protections into the data engineering and model training stages of predictive model development. Our interest is a pragmatic implementation in an operational environment, which necessitates a general purpose differentially private modeling framework, and we evaluate one such tool from LeapYear as applied to the Credit Risk modeling domain. Credit Risk Model is a major modeling methodology in banking and finance where user data is analyzed to determine the total Expected Loss to the bank. We examine the application of differential privacy on the credit risk model and evaluate the performance of a Differentially Private Model with a Non Differentially Private Model. Credit Risk Model is a major modeling methodology in banking and finance where users data is analyzed to determine the total Expected Loss to the bank. In this paper, we explore the application of differential privacy on the credit risk model and evaluate the performance of a Non Differentially Private Model with Differentially Private Model.
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URL
https://arxiv.org/abs/2106.15343
