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The purpose of this guide is twofold: a) to introduce the Fraud Data API project, with its processes and procedures, to college and district staff who will be participating in fraud reporting and data sharing operations; and b) to provide the technical information and API documentation to IT staff and developers who have a higher an understanding of the basic concepts and capabilities of GraphQL API technology, including access to the schema and query templates used to execute the primary operations currently supported by this service.

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GraphQL is a new API standard that provides a more efficient, powerful and flexible alternative to REST (Representational State Transfer)*. GraphQL

While there are many excellent learning guides and tutorials on GraphQL online, including what it is and how to work with it, this document will primarily focus on learning and understanding the query operations of the Fraud Data API - which is developed based on GraphQL technology.

^{*Courtesy of GraphQL EdEx Course}

See the About GraphQL APIpage for an introduction to the basic concepts of GraphQL technology, the schema, and examples of the functional operations used in the Fraud Data API (query, mutation, subscription).

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On the Fraud Report OAuth tab. Step 1: In your Postman Workspace, click on the Collections icon to expand the Fraud Report collection. Step 2: Click on the Fraud Report OAuth file to open the tab in your Workbench. Step 3: Ensure the appropriate environment is selected. In the adjacent example, the “GraphQL:Pilot” environment is selected (active). Step 4: Click the Send button to generate the access token. Access Token The 'access_token” becomes the Bearer required in the Authorization head for secured requests to the API. (Reminder: Your API account is provided by the CCCTC during the implementation process.) Each time you refresh your authorization, the new ‘access_token’ is automatically saved to the header for the next API operation you intend to execute. Each token expires in 300 seconds (5 mins).

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An objective of the fraud data project is to leverage SuperGlue in developing and the College Adaptor to implement more automated processes for bi-directional sharing of fraud information with and between colleges that are receiving fraudulent applications from the same individuals. In the first early phase, CCCTC is able to identify every college that has to broadcast notifications of reported bad actors to any other colleges that have also received applications from the suspect same CCCID, and then notify those colleges that have received an application from the individual associated with a fraudulent application reported by a college. When this happens, when the system identifies other submitted applications to other colleges by the same CCCID, a .

When this happens, a new fraud-report is pushed to a dedicated staging table (for each college) via the SuperGlue College Adaptor. Each notification response contains, among other available fields, the fraudulent contains will identify suspect CCCID and the application ID(s) (AppID), the applicant’s CCCID, and the reporting college’s MIS code of the college reporting the fraud(reportedByMisCode). 

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Example of a list of fraud reports, streamed to recipient college’s sis fraud staging table.

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Querying Fraud Data via API

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