How to use the Arena for exploration of ML models for credit scoring

To develop predictive models responsibly we need good methods and tools to explore and examine predictive models. This leads to a growing interest in eXaplainble Artificial Intelligence (XAI) of Interpretable Machine Learning (IML) techniques. More and more libraries for Explanatory Model Analysis (EMA) are available in the R and Python packages (see shap, lime, dalex). However, using them requires writing code, which in turn slows down feedback and reduces the effectiveness of the exploration process.

Arena provides three observation level XAI charts:

To succeed, she needs prediction above the cutoff level (default 0.5). As you can see on both charts, the leading cause of disapproval is the police record. Sadly, it’s something we cannot change.

By clicking on Break Down contribution blocks we can explore Ceteris Paribus charts for the selected variable. We will check variables, that she is able to improve.

I already annotated (red color) points, where only one change guarantees exceeding the cutoff:

At the moment it is strange, that renting a house will improve credit score, but that is the example why in the real world data scientists need to understand their models.

But Victoria may improve her situation! She should for the sake of taking credit separate with her husband and move to her parents. If she then shortens loan time by 3 months her credit will be approved.

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