Topic outline

  • General

    Financial Fraud Detection

    In this phase of the Big Data Analytics Experience, you will build a predictive model from a dataset of financial transactions. You will analyze these transactions, choose your approach and an appropriate algorithm, and, using machine learning techniques, train and run a predictive model to detect occurrences of financial fraud. Read the directions below and launch your lab to begin!

    • Directions

      Use the button below to launch the Apache Zeppelin lab, then follow the Zeppelin link to your container. Please be aware that the first execution of code in your notebook may take several minutes to run.

    • Challenge Completion

      If you have finished the exercises in the Apache Zeppelin notebooks, select the box below to receive credit for completing the challenge.

    • Next Steps

      What do you think of this example model and its results? Is it convincing? Is there a dataset or data challenge that you would like to work with in future Play experiences? Feel free to share your model, ideas, and experiences with your peers and InterSystems staff via the InterSystems Developer Community to discuss approaches and solutions. The InterSystems IRIS Experience group has been specially created for the InterSystems IRIS Experience. Be sure to tag your post with Big Data Experience to link up with other people in the Big Data Analytics Experience.

      Finally, if you would like to find out more about how you could bring your organization’s data and its challenges to InterSystems, use the Build phase of this Experience to talk to InterSystems about your project.

      • How do I provide feedback on this experience?

        Thank you for completing this challenge. To assist us in making this as helpful as possible for future learners, please complete our reviewer survey, as well. Don't forget to click Done at the end to submit your responses.