[{"_1":2,"_42":-5,"_43":-5},"loaderData",{"_3":4,"_10":11,"_40":-5,"_41":-5},"root",{"_5":-5,"_6":-5,"_7":-5,"_8":9},"user","shoppingCartId","toast","locale","en","routes/_store+/_readers+/email_author.$slug/route",{"_12":13,"_39":-5},"book",{"_14":15,"_16":17,"_18":19,"_20":21,"_22":23,"_24":25},"id","Z2lkOi8vbGVhbnB1Yi9TdG9yZTo6Qm9va1Byb2R1Y3QvNjQzOTM","title","Credit Risk Modeling Working Notes","state","published","contactAuthorMethod","email","aboutText","

These Working Notes compile material from the author's GitHub repository, accessible at the following link. The notes and the GitHub repository are dedicated to two books - Applied Data Science for Credit Risk and Probability of Default Rating Modeling with R - covering different credit risk modeling topics. Their main goal is to extend the content of the books to specific tasks and challenges practitioners face in their day-to-day work.

The motivation behind writing these books and creating the repository stems from the observed gap between academic literature, industry practices, and the evolving landscape of data science. While the literature on credit risk modeling has grown significantly, discrepancies persist. The evolution of data science has brought considerable automation to many processes but has also introduced the risk of overreliance on pre-programmed procedures, sometimes resulting in the misuse of statistical methods. Additionally, many practitioners entering the field of credit risk modeling often overlook fundamental principles, which hinders their professional development.

These Working Notes aim to serve as a centralized hub for continuous education and consolidating essential concepts in credit risk modeling.

Like the idea behind the GitHub repository, the Working Notes will be regularly updated as new material becomes available.

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