Avoiding Discrimination in Unstructured Data

An article published by the Wall Street Journal on Jan. 30, 2019  got me thinking about the challenges of using unstructured data in modeling. The article discusses how New York’s Department of Financial Services is allowing life insurers to use social media, as well as other nontraditional sources, to set premium rates. The crux: the data cannot unfairly discriminate.  

I finished the article with three questions on my mind. The first: How does a company convert unstructured data into something useful? The article mentions that insurers are leveraging public information – like motor vehicle records and bankruptcy documents – in addition to social media. Surely, though, this information is not in a structured format to facilitate querying and model builds.

Second: How does a company ensure the data is good quality? Quality here doesn’t only mean the data is clean and useful, it also means the data is complete and unbiased. A lot of effort will be required to take this information and make it model ready. Otherwise, the models will at best provide spurious output and at worst provide biased output.

The third: With all this data available what “new” modeling techniques can be leveraged? I suspect many people read that last sentence and thought AI. That is one option. However, the key is to make sure the model does not unfairly discriminate. Using a powerful machine learning algorithm right from the start might not be the best option. Just ask Amazon about its AI recruiting tool.[1]

The answers to these questions are not simple, and they do require a blend of technological aptitude and machine learning sophistication. Stay tuned for future blog posts as we provide answers to these questions.

 

[1] Amazon scraps secret AI recruiting tool that showed bias against women

 

Jonathan Leonardelli, FRM, Director of the Business Analytics for the Financial Risk Group, leads the group responsible for model development, data science, documentation, testing, and training. He has over 15 years’ experience in the area of financial risk.

Real Time Learning: A Better Approach to Trader Surveillance

An often-heard question in any discussion of Machine Learning (ML) tools is maybe most obvious one: “So, how can we use them?”

The answer depends on the industry, but we think there are especially useful (and interesting) applications for the financial services sector. These consumers have historically been open to the ML concept but haven’t been quick to jump on some potential solutions to common problems.

Let’s look at risk management at the trading desk, for example. If you want to mitigate risk, you need to be able to identify it in advance—say, to insure your traders aren’t conducting out-of-market transactions or placing fictitious orders. The latest issue of the New Machinist Journal by Dr. Jimmie Lenz (available by clicking here) explains how. Trade Desk Surveillance is just one way that Machine Learning tools can help monitor a variety of activities that can cause grief for those tasked with risk management.

Would you like to read more about the possibilities ML can bring to financial services process settings? Download “Real Time Learning: A Better Approach to Trader Surveillance,” along with other issues of the New Machinist Journal, by visiting www.frgrisk.com/resources.

Introducing the New Machinist Journal

Who are the new machinists, and what are their tools?

The machinists of the 21st century are working with Artificial Intelligence (AI) and Machine Learning (ML), turning what has been science fiction into science fact. From learning algorithms that nudge us to buy more stuff to self-driving vehicles that “learn” the highways and byways to deliver us to our destinations safely, AI and ML are attracting considerable attention from a variety of industries.

FRG is currently researching and building machine learning proof-of-concepts to fully understand their practical applications. A new series, the New Machinist Journal, will explore in detail some of these applications in different environments and use cases. It will be published regularly on the FRG website. Volume 1, “What Artificial Intelligence and Machine Learning Solutions Offer,” is an overview of the subject, and is now available for download (click here to read it).

For more information, contact the FRG Research Institute, Research@frgrisk.com

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