Explorium Resources

  • How Can You Enhance Your Risk Models? Finding Better Signals to Feed Them

    How Can You Enhance Your Risk Models? Finding Better Signals to Feed Them

    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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  • 6 Steps to Jumpstart Machine Learning Using the Resources You Already Have

    6 Steps to Jumpstart Machine Learning Using the Resources You Already Have

    ML has gone from buzzword to business necessity, and implementing it is quickly becoming mandatory. Here are 6 easy steps to follow to get going with the resources you have.

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  • Why You Need Data Catalogs, Not Databases

    Why You Need Data Catalogs, Not Databases

    When it comes to external data for machine learning, data catalogs provide a handful of time-saving benefits over databases. Learn more.

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  • Where’s the ROI? Demonstrating the Value of Data

    Where’s the ROI? Demonstrating the Value of Data

    Leaders must find a way to prove, tangibly, that embracing data and investing in better ways to use it has true value and real ROI.

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  • How to Improve Your Training Data for Vastly Better Machine Learning

    How to Improve Your Training Data for Vastly Better Machine Learning

    Making your training data better is much easier than you think, and you can use several easy strategies for quick wins.

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  • Using External Data to Future-Proof Your Organization and Ensure Success Today and Tomorrow

    Using External Data to Future-Proof Your Organization and Ensure Success Today and Tomorrow

    If the current economic crisis has shown the business world anything, it’s that no amount of data analysis can prepare you for the event of having the financial market flipping upside down.

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  • Learn to turn the "art" of feature generation into a science.

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  • Could Walter White and Marty Byrde Fool The Best Risk Models?

    Could Walter White and Marty Byrde Fool The Best Risk Models?

    Walter White and Marty Byrde are known for their money laundering schemes. But could they really fool the best risk models banks have today? Let's look.

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  • Mitigating Risk With External Data, A Guide For CROs

    Mitigating Risk With External Data, A Guide For CROs

    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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  • Why Data Marketplaces Are the Future of the Data Economy

    Why Data Marketplaces Are the Future of the Data Economy

    Data marketplaces make the lives of data scientists looking for machine learning datasets much easier. Read how.

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  • How Our COVID-19 Signals Give Businesses Better Decision-Making Capabilities

    How Our COVID-19 Signals Give Businesses Better Decision-Making Capabilities

    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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  • Marketers and Data Science: Tapping Into The Data You Need to Build a Smarter Marketing Organization

    Marketers and Data Science: Tapping Into The Data You Need to Build a Smarter Marketing Organization

    In these increasingly uncertain times, marketing leaders who start thinking data science-driven will not only stay ahead of the pack but also keep their organizations afloat.

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  • Want to Get People Excited About Your Machine Learning Project? Tell Them a Story

    Want to Get People Excited About Your Machine Learning Project? Tell Them a Story

    Top tips to engage stakeholders at every stage of the data science project life cycle.

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  • How Data and Analytics Leaders Can Avoid Roadblocks to Data Acquisition

    How Data and Analytics Leaders Can Avoid Roadblocks to Data Acquisition

    Data acquisition is a long and time-consuming process that many data and analytics leaders don't have time for. How can you streamline it for a major impact?

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  • How to Connect to the Data Ecosystem

    How to Connect to the Data Ecosystem

    In this article, we explain where external data comes from, the key challenges, and how to connect and utilize it with data science solutions.

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  • Data Bias and What it Means for Your Machine Learning Models

    Data Bias and What it Means for Your Machine Learning Models

    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.

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  • COVID-19 Broke Your Risk Models. This is How External Data Can Fix Them.

    COVID-19 Broke Your Risk Models. This is How External Data Can Fix Them.

    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.

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  • 3 Use Cases Risk and Fraud Leaders Must Implement Today

    3 Use Cases Risk and Fraud Leaders Must Implement Today

    CROs need to upgrade their visibility, which requires looking outside their organizations for data. But, how can they be effective when collecting external data can take weeks or even months?

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  • Tomorrow Comes Today – Using External Data to Cut Through the Unpredictability of Crises

    Tomorrow Comes Today – Using External Data to Cut Through the Unpredictability of Crises

    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 –

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  • Fraud Has a Bigger Impact in Crises – Can You Afford to Sleep on It?

    Fraud Has a Bigger Impact in Crises – Can You Afford to Sleep on It?

    During crises, risk takes on a whole new meaning, and assessing it becomes more important, but also much more complex.

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  • 4 Practical Ways Marketers Benefit From Data Science-as-a-Service

    4 Practical Ways Marketers Benefit From Data Science-as-a-Service

    Marketers can't afford to waste time piecing together data for insights. Data science-as-a-service offloads this entire problem for better decisions.

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