Create models and use custom H2O recipes specifically built for generating features/variables that provide associated information about defaulting behavior.
Make data available to the credit officer who can further slice and dice the data and consume the information intuitively.
H2O AI App built with Wave, an SDK for Data Scientists, can perform:
Supervised Machine Learning
Interactive Component: users can upload the training dataset to train the model first, then upload the testing data and view the results.
The results viewed could be in tabular form, grouped results ( all defaulters, people of xyz location,etc) or a dashboard for a single customer.
Dashboard to display insights like percentage chance of defaulting and graphs ( eg - scatter plot of all customers, position of selected user wrt others), the rate of interest charged/the return expected for specific customer, and a textual insight explaining the prediction for better transparency.