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USE CASE

Fair Lending and Loan Predictions

Objective

  • Determine lending risk and payment default rates in situations with limited or non-existent credit bureau utilization

  • Simplify the process of predicting whether the customer will default on their credit or not, minimizing losses. 

Outcome

  • Accurate models that will predict risk profiles for customers based on their Customer 360

  • Model Explainability that provides reasons behind the predictions of deployed models

  • Generate profits for the institution through differential borrowing/ lending rates.

Business Value

  • H2O.ai saved $20M per year in credit underwriting for single customer

  • More accurate predictions of bad credit and with high probability of defaulting

  • Increased accuracy in interest rates and expected returns for each customer, according to their risk profile

H2O's AI and Data Approaches

  • 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:

    • Data Preparation

    • Non-algorithmic modeling

    • 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.

Resources

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