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Automated Document Processing for Human Services and Labor

Automated Document Processing for Human Services and Labor

Challenge: Government agency programs are swimming in complex applications, verification documents, reapplications, etc. Often the processing is manual or partially automated requiring significant staff time which can lead to backlogs and delays in benefit(s) delivery. This is especially problematic with the increase in citizens needing benefits due to COVID-19.  The White House Office of Information and Regulatory Affairs reported that paperwork costs the government $38.7 billion and the public $117 billion each year. At this scale, eliminating more than paper and investing in modern digital citizen experiences can drive real, lasting change.

 

Google Solution: Google’s automated document processing solution automates the majority of the entire process, leading to a 70%+  reduction in manual processing. Automated document processing digitizes documents--both typed and written, extracts data, verifies authenticity, matches, indexes and uploads data to the core case management system saving staff time and providing benefits to citizens faster.  It offers an end-to-end solution that not only automates data extraction and entry but manages the workflow of documents and dashboards on status and age and enables quicker outreach to constituents for incomplete or missing documents and information. The automated document processing solution can be used for services like invoice/payroll, insurance forms, human resources data, fraud detection, mortgage and lending.

 

Reference Materials

 

Where is this working

 

Partners: A system integration partner is required to deploy or customize this solution.

Quantiphi has been the primary partner in the development of this solution; however, other Google certified partners will have the knowledge and ability to deploy the solution as well. 

 

Contact your Google representative or reach out to the Google Cloud team to learn more.

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Last update:
‎02-25-2022 07:58 AM
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