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Fraud managers are busy. We know it's hard to find a one-stop resource shop for determining how to choose the right fraud tools that align with your fraud strategies and organization goals. The Rippleshot team is here to be your guide to approach this complex space.

Recently, we've put together a few short pieces on how to navigate the fraud solution ecosystem, including some actionable tips on what you should consider when selecting the right path for your organization. Below are three resources we've put together to help your team get on the right path to lowering fraud costs and hitting your goals.

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Evaluating Fraud Analytics Tools: 5 Features to Consider

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The pain points among fraud professionals are all too common. They are burdened with evolving fraud threats, compliance challenges, and an inability to know what fraud risks are coming and what high-dollar events might hit next. Luckily for financial institution leaders, the evolution of cloud technology, enhanced data security and the application of AI and machine learning technology has paved the way for fraud analytics tools to address these pain points.

To help guide your decision making process as you ramp up your fraud mitigation efforts, we’ve broken down 5 questions worth asking yourself when determining how to choose a fraud analytics tool. Read more.

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Fraud Detection Rule Writing Assessment: Measuring Your Effectiveness

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At smaller financial institutions, fraud analysts and managers often have a small team, or sometimes are a “team” of one. The fraud rule writing processes they manage are tedious, time-consuming and hard to judge if they are actually effective. Frauds can be easily overlooked without the right tools in place.  We're here to share the common pain points we hear about fraud rule writing and how you can assess your fraud mitigation efforts.

Fraud analysts and managers often struggle to adapt quick enough to emerging fraud risks due to lack of tools, access to enough data, and simply enough hours in the day. Call centers are juggling priorities - creating organizational silos and poor intel into where the greatest fraud risks exist. We've put together 5 questions you should be asking yourself as you continue to develop your fraud rule writing processes. Read more.

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Guide: Why Fraud Analysts and Managers Need Machine Learning and AI Tools

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There's a lot of noise in the AI and machine learning space. We're here to help you cut through that noise and learn why advancements in big data can help your fraud team streamline operations, lower fraud costs and minimize customer impact.

The bigger banks with deep pockets are gaining a FinTech edge with teams of data scientists and sophisticated software tools to keep their fraud detection tools aligned with what the market demands. Smaller FIs know to compete they must embrace new technologies such as AI and machine learning. We've broken down where financial institutions leaders can proactively start thinking about how to protect their customers and members.  Read more.