For lenders, pre-COVID-19 data is no longer useful in a post-COVID-19 world. Here's how we're giving our cu...
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In this in-depth guide, we reveal the real reasons your machine learning risk models are falling short, what you can do to fix them — and exactly how to tackle the problem.
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Your organization’s risk management strategies are going to need a major overhaul. Insights from your historical data simply won’t be enough to help you assess the risks that are coming your way.
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With ML models rendered useless, we built an entirely new set of COVID-19 signals in our platform that let organizations understand their risk derived from the current pandemic.
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In this article, we explain where external data comes from, the key challenges, and how to connect and utilize it with data science solutions.
Let’s take a look at some of the most prevalent types of bias, the data mistakes that cause them – and how to prevent this from happening in your own models.
For lenders, pre-COVID-19 data is no longer useful in a post-COVID-19 world. Here's how we're giving our customers external data to ensure their risk models work in our new reality.
It’s time to look outside of our own silos to external data and make sure that despite the crises we face, we can give our organizations the power to make it through. The post Tomorrow Comes Today –
During crises, risk takes on a whole new meaning, and assessing it becomes more important, but also much more complex.
Marketers can't afford to waste time piecing together data for insights. Data science-as-a-service offloads this entire problem for better decisions.
From understanding your own business to better predicting uncertain conditions around you, these are just a few ways data leaders can use external data for better business decisions.