All SNAP applicants upload images to support their applications. Agency staff need to manually verify and parse images, which can lead to delays, missed deadlines, and errors. 

Our Intelligent Document Processing (IDP) solution uses smart image technology to improve image quality and data accuracy. 

Example features

Image assessment

• Prevents image degradation during upload

• Checks image quality in real-time

• Provides users with immediate feedback and guidance if issues are detected

Automatic data entry

• Uses Optical Character Recognition (OCR) to “read” the uploaded documents and convert image data into a format your system can easily process

• Automatically extracts and sorts key information and fills out relevant fields in the application

Example outcomes

• Fewer errors due to poor image quality

• Reduced workload for caseworkers

• Streamlined application process

When applicants don’t know the status of their claim, they might submit duplicate claims or contact the call center multiple times. This can confuse applicants and strain call centers. 

Nava will help you implement a self-service claim status tracker that enables applicants to easily check their SNAP applications and renewals. 

Example features

• Ability to check claim status by providing key personal information; no login required

• Detailed claim status updates

• Instructions on next steps, such as submitting documents or attending a phone interview

• Ability to submit documents directly from the tracker

• Instructions on what to do if the system can’t find your application

Example outcomes

• Fewer duplicate claims

• Reduced call center volume

• Better user experience

Typically, there’s a 10 to 30-day window between case authorization and formal quality control review. This is an ideal time for agency staff to identify and fix errors. However, agency staff must review cases manually, which can slow the process of identifying and resolving errors. As a result, agency staff are overextended and struggle to properly review cases before quality control (QC) begins. 

We are building a reporting system that automatically checks for common errors. This technology will use rule-based logic to identify discrepancies and errors in applications. Then, it will create a pre-scanned, prioritized list of high-risk applications for agency staff to vet.

Example features

• Ability to identify, add, or track SNAP rules

• Simple UI with filters and discrepancy detail modals

• Ability to take action on a high-risk claim

Example outcomes

• Streamlined review process that helps staff prioritize critical, high-risk cases

• More efficient error identification before a claim hits QC

The SNAP error rates dashboard helps states analyze data to inform activities that can help reduce their SNAP error rates. Created in partnership with Georgetown University and the University of Michigan Better Government Lab (BGL), the dashboard can help answer questions such as: 

• I found a really expensive error. What can I learn about these cases to prevent them in the future?

• How do error rates in my state compare with other states?

• Does my state have a particular population that is missing out on deductions, leading to underpayment errors?

The open data in the Metabase tool is part of the SNAP quality control error viewer, an application that Zhaowen Guo at the BGL created to make SNAP error data more actionable. To answer state-specific questions and create custom visualizations, you will need access to the Metabase tool. Please fill out the contact form below for our team to share free, unlimited access.