COMPLETING THE PEP PRODUCT AND EXECUTING PROJECT PARAMETERS
Our project parameters were outlined as follows, dictating a two-prong approach. The proposed system required the following functionalities:
1. Enhance and enrich the existing PEP Profile.
- Search for the matching media articles in the source database based on the keyword provided.
- Store the reference of the matching records in new database table.
- Loop through each of the existing PEP Profiles and use the Rosset Name Matching tool to find the matching name.
- If a match is found, use the Rosette Entity matching tool will match the different entity of the PEP profile holder.
- Enrich the findings by updating the entity into existing PEP Profile database fields.
2. Create New PEP Profile:
- Use the newly stored media articles from Step 1.
- Identify new individuals who hold positions of authority.
- Identify the individuals name associated with each keyword.
- Compare with existing PEP Profile to check if the individual has an existing PEP Profile.
- If the system finds an individual’s name and position of authority, then it should further compare the positions (Country and State/Region). If no match is found, then discard that article and move into the next article /record
- If PEP Profile does not exist in the system, or if it exists and the position and the location does not match, the system should create a new PEP profile.
We engaged with five different modules during our project with Vital4. These modules included a Service Module, PEP Profile Enrichment Module, PEP Profile Creation Module, Rosset Text Analytics, and PEP Database.
Service Module: This module will contain the basic logic required to process media files from the starting /entry point.
Profile Enrichment: This module will be responsible for capturing the media-relevant files based on the keyword defined and use the Rosette Text Analytics to match the name and entity and update the existing profile with different entity.
PEP Profile Creation: This Module will be responsible for identifying the media articles that are related with a particular position and name and use that media to create the basic PEP Profile.
Rosette Text Analytics: This Module already exists and is running on production above both module will use this to get the results of Name and Entity Matching.
Database: Database where Profile Enrichment and PEP Profile Creation module will update/create the new profile data.
If we zoom in on the PEP product, we can dissect it into three layers: the presentation layer, a business layer, and the data access layer. We built this hierarchy to forge sustainability—the component-level design supports scalability and incremental enhancement.
For the development framework, we went with Visual Studio as the IDE, C# as the programming language, and SQL as the Database. The console application is compatible with Windows operating system, and supports a variety of browsers including Chrome, IE, Firefox, and Safari.