Case study: Community Brands cuts document verification from 2 weeks to less than 2 days
Too often, a single obstacle can delay a coordinated effort to scale business operations. For Community Brands` Financial Aid division that obstacle was document verification, one of three objectives that comprise the company’s Financial Aid offering.
In the business cycle for K-12 Financial Aid, private schools appropriate the funds they have to distribute to eligible families of qualifying students. Eligibility is determined by the financial information each family enters in the online application, which Community Brands must then verify using the supporting financial documents the family provides.
Challenges: capacity, accuracy, speed & data privacy
The primary challenge of this business cycle lies in processing capacity. Families submit financial aid applications once annually—just before the school year begins—the busiest time in terms of administrative workload. Even assigning full-time employees to the sole task of verifying the information in the applications was insufficient. Matching the data from the documents to that in the application would take 10-12 business days per application. Furthermore, manual verification of financial documents was 70% accurate at most, and lastly, having an individual handle confidential documents in their entirety is not best practice in terms of data privacy.
Safe & infinitely scalable document verification
Community Brands needed a solution for document verification to provide their K-12 customers with quick, accurate and confidential processing of financial aid applications. With ScaleHub’s crowdsourcing solution, Community Brands got everything they needed: the scalability, accuracy and confidentiality that financial aid applications require.
How it works: step by step
This infographic outlines Community Brands’ new solution for document verification. Today, applicant families upload their documents directly to Community Brands’ portal, then:
- The documents are automatically classified using a self-learning technology
- The data is randomized into non-contextual snippets
- Highly sensitive information, like social security numbers, is scrambled for full security
- The snippets are sent to the crowd where they are verified using a secure web browser
- All data is exported and returned to Community Brands for final processing