Zest Insights

Does Model Risk Management Have to be Such a Nightmare?

Posted by Adam Kleinman on May 21, 2018 10:25:43 AM

Lenders’ traditional Model Risk Management (MRM) process was set up for conventional models based on logistic regression. Model verification, fair lending analysis, and model risk management documentation are already onerous for these traditional models. Machine learning (ML), which can use thousands of variables and multiple modeling methods, is likely to present a significant challenge for your organization’s established manual MRM process. Do the same principles of MRM apply for ML models? Does the MRM process for ML models need to be as time-consuming and laborious as it has been in the past?

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Four Tips For Establishing an Automated Model Risk Management Plan

Posted by Adam Kleinman on May 14, 2018 3:41:06 PM

The benefits of using automation in the model risk management (MRM) process are significant for organizations transitioning to machine learning-based credit underwriting technologies. Automation improves operational efficiency and allows your organization to keep up with changing market conditions. An automated MRM process also facilitates knowledge transfer to new employees and provides your regulators with all the information they need to verify your model. So, how do you make sure that you have a robust plan in place for automated MRM?

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What You Don't Get With a World Class Modeling Tool

Posted by Douglas Merrill on May 7, 2018 11:52:58 AM

The dirty little secret of machine learning is that the algorithms are the easy part. Not to say that math is simple, but rather that there are great books1 and tools2 to help you build just about any model you want. Instead of worrying about the algorithm, per se, you should worry about how to get that model into production.

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The Real World of Predictive Modeling: Best Practices for the New Data Scientist

Posted by Armen Donigian on Apr 16, 2018 10:40:16 AM

Whether you’re a recent college graduate or looking to switch careers, moving to the real world of predictive modeling can be intimidating for the new data scientist. It means the models you build will actually be put into production. Every decision you make, from defining the problem to deploying the model, becomes critically important, especially in high-stakes industries like finance and healthcare.

Here’s a set of guidelines to help you successfully translate your new skills into the real-world of 24/7 automated decisioning using predictive modeling and analytics.

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3 Tips to Consider Before Using Alternative Data in Underwriting

Posted by Jay Budzik on Apr 12, 2018 10:59:39 AM

More data is available for use in lending than ever before. In recent years, alternative data providers have proliferated. But it can be daunting to determine which data sources are worth evaluating. Here are some tips to consider before you begin your search for credit data providers.

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Explainable Machine Learning in Credit

Posted by Jay Budzik on Mar 13, 2018 9:00:00 AM

Machine learning is being used to solve an astonishing array of previously unsolvable problems. Besides powering search results and internet advertising, machine learning is used to help computers hear and see. Machine learning can recognize voices and characters, automatically label your personal photo library, pick the right music, assign jobs to the right worker, and help drivers prevent their car from veering out of their lane, among many other things.

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6 Methods for Dealing with Missing Data

Posted by Bojan Tunguz on Mar 5, 2018 10:19:37 AM

Most datasets in the real world contain missing data, incorrectly encoded data, or other data that cannot be used for modeling. Sometimes missing data is just that — missing. There is no actual value in a given field; for example: an empty string in a csv file. Other times data is encoded with a special keyword or a string. Some common encodings are NA, N/A, None, and -1. Before you can use data with missing data fields, you need to transform those fields so they can be used for analysis and modeling. There are machine learning algorithms and packages that can automatically detect and deal with missing data, but it’s still a good practice to transform that data manually.

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AI Will Take Over Credit Decisions… And That’s Good

Posted by Douglas Merrill on Feb 22, 2018 1:46:36 PM

When the first Model T drove out of the factory in 1908, it was pretty clear something big was happening. If you had asked someone then whether cars would replace horses for transportation by 2010, even without the benefit of hindsight the answer would be "Duhhh." Like the assembly line before it, artificial intelligence (AI) will transform nearly every industry by making powerful technology ubiquitous. Yet the talking heads are still asking: will AI take over heavily regulated high stakes tasks like credit underwriting? (Spoiler: yes, it will, and it already is.)

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Machine Learning in Credit. Today.

Machine learning can be explainable, useable, incredible.

Zest Insights brings you the latest in machine learning and credit underwriting. We believe in a world where everyone has access to fair and transparent credit, and machine learning is the way to get us there. Machine learning is changing every industry, and now is the time for lenders to bring this new predictive power into their businesses. 

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