  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.
