Risk and fraud leaders

Resources for data science-driven risk and fraud leaders.

  • Should You Use ML To Predict Small Business Default Risk?

    Should You Use ML To Predict Small Business Default Risk?

    In this blog post, we explain how lenders can use machine learning to build more accurate, reliable, cost-effective risk models.

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  • 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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  • How To Include Data Science Platforms in Your 2021 Budget

    How To Include Data Science Platforms in Your 2021 Budget

    In this article, we explain how to know if you need a data science platform, how to approach questions of costs and savings, and what to consider when calculating your TCO.

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  • Making Alternative Credit Scores the Norm: How to Create a New Scoring Model

    Making Alternative Credit Scores the Norm: How to Create a New Scoring Model

    The current credit scoring model is outdated and in need of an upgrade. Read how to go about building a smarter, more accurate credit scoring model - and the data you need to do so.

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  • To Really Know Your Customer, You Need A Risk Modeling Platform Fueled by External Data

    To Really Know Your Customer, You Need A Risk Modeling Platform Fueled by External Data

    In this article, we explain why you should use external data for effective Know Your Customer (KYC) programs. The post To Really Know Your Customer, You...

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  • Should You Be Using Traditional Credit Scores?

    Should You Be Using Traditional Credit Scores?

    In this blog post, we look at the shortcomings of conventional credit scores and ratings and explain how to improve your models.

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  • Where's the Weakest Link? Understanding Risk in Supply Networks

    Where's the Weakest Link? Understanding Risk in Supply Networks

    In our interconnected, globalized world, it’s harder and harder to track the weak spots in your supply chain. That’s why we created this handy guide to understanding and mitigating supply chain risk.

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  • What Happens When The Definition of Risk Changes?

    What Happens When The Definition of Risk Changes?

    Risk and probability are based on data. You establish the statistical risk of losing an investment based on other investments that didn’t work out. You assess The post What Happens When The...

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  • Could ML-Powered Risk Models Really Spot OZARK’s Schemes?

    Could ML-Powered Risk Models Really Spot OZARK’s Schemes?

    In some ways, OZARK’s main plot seems too good to be true, and it might actually be. In the real world where ML-powered risk models exist, could Marty Byrde really get away with his schemes?

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  • How Dynamic Risk Modeling Gives Your Detection a Boost

    How Dynamic Risk Modeling Gives Your Detection a Boost

    Dynamic risk models add a dimension to rules-based risk modeling that makes you more agile to the changing world.

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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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  • 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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  • 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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  • 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 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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  • 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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  • Explorium: Product Overview1:03

    Explorium: Product Overview

    Explorium is the future of data science. We enable you to automatically connect to thousands of data sources, distill the best features, and deploy the best models.

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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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  • AI Across the Funnel: The Use Cases You’re Missing Out On

    AI Across the Funnel: The Use Cases You’re Missing Out On

    Building core operations around ML models has helped many companies thrive across many other verticals. Still, others wait to implement at a large scale. Why?

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