LendingClub was founded in 2006 as an alternative, peer-to-eer lending model to connect individual borrowers to individual investor-lenders through an online platform. Since 2014 the company has worked with institutional investors at scale. While the company assigns grades and sub-grades to each application using its own risk evaluation model, it also makes detailed data on each loan applications available to both kinds of investors for their own analyses. The case follows MBA graduate Emily Figel as she researches LendingClub as a potential investment vehicle for the small wealth management firm she will join in the fall. Using LendingClub's historical data, she learns the fundamentals of predictive analytics to see whether she can build models to predict whether a borrower will repay or, ultimately, default on the obligation. This first case (A) presents students will relevant, detailed data about how the LendingClub model works. This includes LendingClub's business model, the grading of loans, the unique opportunities and risks. It also follows Figel as she dives into the data to use it to build a model. In the B and C cases, Figel explores several specific techniques for training models. Technical topics include: understanding the data, data preparation, balanced and unbalanced data sets, constructing training-validation-holdout sets, cross-validation, predictions and target leakage.

【書誌情報】

ページ数：18ページ

サイズ：A4

商品番号：HBSP-119020

発行日：2018/8/22

登録日：2018/12/5