Andrew Flint

Scorecards and Machine Learning: Replace or Embrace?

Blog Post created by Andrew Flint Advocate on May 22, 2018

We all know that scorecards can be excellent predictive models, lauded for their combination of simplicity, transparency, and accuracy. And we also love machine learning algorithms for their highly automated and remarkably thorough search for signal. So if you’re already building random forests, gradient boosted trees or neural networks in your analytic projects, is there still a role for scorecards?

 

I’ll admit this isn’t the first time, the second time or even the third time we’ve entertained this question, but the answer remains a resounding “yes.”

 

Scorecards and Machine Learning: A Potent Partnership

Scorecards, and especially segmented scorecards, are a phenomenally efficient and transparent means to create strong predictive models that everyone can immediately understand. This is particularly true when you know the data well and have a great set of predictive features to model from.

 

Scorecards are also incredibly helpful for bolstering our knowledge of our customers and their behaviors. Because they actively engage a human in the loop during the model development process, scorecards help the data scientist quickly learn about the multivariate relationships in the data, which can in turn lead to better predictions and decisions downstream.

 

On the other hand, machine learning methods can be highly automated, and don’t even really afford the chance (or need?) for the data scientist to carefully inspect every aspect of the resulting model. In their search, machine learning techniques are often very thorough, and can automatically capitalize on hidden, latent, non-additive features in the data.

 

But here’s the rub: if you want to understand the insights captured within the machine learning model, you may be out of luck, because common ML libraries simply cannot reveal them to you. Without a healthy dose of what we call “xAI” (explainable artificial intelligence), it’s nearly impossible to know what’s actually happening inside that machine learned model.

 

supervised learning_ML v Scorecard.png

 

In the upcoming release of FICO® Analytics Workbench™, analysts and data scientists will have machine learning, deep learning and xAI techniques at their fingertips — right beside our advanced, leading techniques for scorecard development — allowing them to build, deploy and explain ever-stronger prediction machines.

 

We’re excited to share some of the details behind these methods and show you the new and improved scorecards in action. Join our complimentary webinar, May 30 at 1:30pm PST, presented by myself, Andy Flint, and Lamar Shahbazian, FICO’s Analytic Tools product managers. In the webinar, we’ll discuss:

 

  • The pros and cons of scorecard and machine learning techniques
  • What is a scorecard and why is it valuable
  • How you can incorporate machine learning to amplify predictive strength
  • How to combine these techniques for stronger models, without losing explainability
  • The empowering new design for scorecard development in FICO® Analytics Workbench™

 

Click here to register for this free webinar on May 30, 2018.

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