1. Using Pipeline Designer
  2. Managing Scenario Pipelines
  3. Widgets in Scenario Pipelines
  4. Using High Level Dataset

Using High Level Dataset

You must add a high level dataset to begin a scenario pipeline.

To add a High Level Dataset (HLD) and begin a scenario pipeline, follow these steps:
  1. Navigate to the Pipeline Designer page.
  2. Click WidgetsNavigation iconon the upper left corner. The list of pre-configured HLDs is displayed. FCCM TM Cloud Service comes with the following pre-configured High Level Datasets (HLD).
    • External Entity Transaction
    • Transaction by Customer
    • Transaction by Account
    • Insurance Policies by Customer
    • Loans by Account
    • Loans by Customer
  3. Drag and drop the required HLD to the designer pane.
  4. Hover on the HLD widget and click Edit Edit icon. A dialog box is displayed.
  5. Specify the required details.
    • To add a Runtime Parameter, click Add Runtime Parameter and specifiy the required details. For more information about how to create runtime parameters, see Creating Runtime Parameters.
    • To add a Filter, click View filters to view the list of Filters available for this HLD and select the necessary filters. All filters that are available for use in the HLD can be found in the List of filters. You can select any of these filters to be used in the scenario. For more information about how to configure filters, see Configuring Filters.

      Note:

      The Transaction Code filter requires additional steps before using in the scenario. The values for this filter can be provided in two ways:
      • Use the runtime parameter option as given above.
      • Use the list of predefined values. These values are specific for each Oracle customer, as populated in the DIM table..
  6. Click Save Save icon to save the changes. The HLD is saved.

    You can perform certain tasks that are common in all the widgets, such as edit, delete, filter, and so on. For more information, see Common Tasks.

    Add Additional High Level Datasets

    You can add multiple conditions within the dataset to be considered by the scenario when detecting behaviors of interest. This can help improve the accuracy of your detection results and reduce false positives. For more information, see Adding Additional Threshold Conditions.