Explorium Blog

  • How Dynamic Risk Modeling Gives Your Detection a Boost

    How Dynamic Risk Modeling Gives Your Detection a Boost

    It’s not an overstatement to say that detecting risk is a tough task, even with the best technology available. The changing face of fraud, supplier risks, The post How Dynamic Risk Modeling Gives...

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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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  • 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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  • 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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  • 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 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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  • 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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  • 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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  • 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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  • 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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  • 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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  • 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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  • 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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  • How SMBs Can Boost Their Bottom Line With External Data

    How SMBs Can Boost Their Bottom Line With External Data

    There are some insights that can only come from the data you produce or collect in-house. Historical sales figures, for example. Foot traffic through your store. The post How SMBs Can Boost Their...

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  • How Much For The Set? How You Can Monetize Your Datasets

    How Much For The Set? How You Can Monetize Your Datasets

    Data science websites overflow with tips for organizing and handling your data. The question is, how can you take this clean data and monetize it?

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  • Understanding and Handling Data and Concept Drift

    Understanding and Handling Data and Concept Drift

    Over time, ML models start to lose predictive power due to a concept known as model drift. How can you spot data and concept drift and avoid it? Read more.

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  • Stay Inside The Lines: Coloring With Artificial Intelligence

    Stay Inside The Lines: Coloring With Artificial Intelligence

    Can you teach AI to color better than a human? Using a generative adversarial network we can certainly try.

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  • Support and Coverage – Data Integration Metrics You Should Know

    Support and Coverage – Data Integration Metrics You Should Know

    Data enrichment is a crucial step in the modeling process that data scientists tend to overlook due to the difficulty in finding and utilizing external sources.

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  • Clustering — When You Should Use it and Avoid It

    Clustering — When You Should Use it and Avoid It

    Cluster analysis is an essential tool for data scientists but it shouldn’t be your only one. Discover when you should, and shouldn’t, use clustering.

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