By Jerrad Lee and Amelia Wellers     
 

Robotic Process Automation (RPA) is a software solution that makes it easy to build, deploy, and manage virtual robots (also known as “bots”). In recent years, the unemployment insurance (UI) sector has adopted RPA as a flexible productivity tool to reduce or eliminate manual and repetitive processes performed by staff. These bots work at the user interface level to mimic routine staff interactions with existing systems and software. Bots can recognize what is on a screen, perform data entry, navigate and bridge the gap between systems, identify and extract data, complete non-discretionary tasks, and perform a wide range of other well-defined actions quickly and accurately. RPA frees up staff to focus on performing more complex tasks requiring intervention, including discretionary decisions that must be handled by state merit employees (commonly referred to as “merit staff”).

These RPA solutions can address both short- and long-term needs. RPA can be deployed as a rapid solution to a change request to an existing system that is too costly or slow to develop, or when there is a temporary change in operation. RPA may also be an agile solution to add modularity and flexibility to a state agency’s software suite in the long term.

Through technical assistance initiatives with states, UI modernization teams within the U.S. Department of Labor (USDOL) observed states seeking out RPA as a tool to help them reduce administrative burdens associated with UI appeals workflows. This blog post will introduce lessons that we have learned about how RPA is well suited for streamlining the appeals process. 

Administrative challenges in appeals

Appeals processing is an area of UI that is simultaneously highly complex yet frequently manual and repetitive. From processing a potential letter of appeal to distributing a hearing officer’s decision or order, appeals staff must perform many tasks to ensure that the legal rights of an appellant are upheld.

Here are a few examples:

  • Appeals cases involve pulling together data from separate benefits and tax software systems.  
  • Appeals cases can involve more documents and data than initial claims processing.
  • Appeals cases require scheduling hearings, sending notifications to parties, compiling packets of documentation, and coordinating hand offs between different teams. 
  • Appeals staff must keep up to date with diverse administrative and policy changes.  For example, when the Pandemic Employment Assistance (PUA) program was rolled out during the COVID-19 pandemic, appeals staff needed to learn and integrate new policies with their existing processes, as well as process PUA-related appeals, redeterminations, and reopens on top of a spike in overall pandemic-related appeals. These changes often happened quickly, before some IT teams could update their benefits systems.

Why RPA is a good solution for appeals use cases

RPA is well suited for multi-step, detailed, and repeatable processes. Bots can perform and repeat any number of discrete system-related tasks within a flow, thereby lowering the staff administrative burden. Because bots can perform tasks across multiple systems, RPA reduces handoffs. RPA systems can also include built-in staff checkpoints when needed to ensure quality decision making.

For example, RPA can help schedule appeal hearings. Scheduling often involves multiple factors that are difficult to completely account for via traditional software and are time-consuming for staff to handle manually. This may include managing individual merit staff availability, directing certain issue types to specific merit staff, and accounting for priorities around which kinds of cases should be scheduled first. An RPA system can easily navigate these factors based on predefined rules inputted to bots, helping decrease the administrative, manual tasks that staff need to handle. 

RPA also lends itself well to appeals systems because of its relatively low costs.  RPA is often developed using plug- and- play bots with very little custom coding needed from a software developer. RPA works on the front-end user interface level, without touching the backend logic of appeals systems, making RPA solutions substantially faster and cheaper to implement than traditional technology solutions. Accordingly, RPA can help states reduce costs and make quicker pivots to address programmatic shifts. RPA enables significantly better utilization of funds and allows merit staff to focus on making determinations instead of spending time on repetitive, non-discretionary, administrative tasks.

How are states currently using RPA in appeals?

During the surge in UI claims due to the COVID-19 pandemic, many states received more appeal letters than staff could analyze in a timely fashion. Upon examination, many of those potential letters of appeal were revealed to be complaints about agency contact, duplicates of prior received appeal letters, or other non-appeal issues. The Georgia Department of Labor implemented an RPA solution to identify relevant documents using pre-determined keywords, compile a packet of appeals documents, and filter out invalid or duplicate documents to help staff more quickly prioritize and process appeals. 

The Colorado Department of Labor and Employment has a robust RPA governance practice, including bots developed to aid appeals business processes. For example, they use bots to automate the party check-in process for appellate hearings, and to send email notifications and reminders for upcoming hearings.

The Virginia Employment Commission went live with a modernized system in November 2021. In the new system, staff must toggle and upload data across three screens when uploading and distributing decisions, slowing down the processing of a single appeal and contributing to a growing backlog of claims. Working with USDOL, the state determined that this area could benefit from RPA and began to develop bots to upload and distribute decisions and orders with greater speed, allowing merit staff to focus on more complex and discretionary tasks.

This year, USDOL also actively collaborated with New Hampshire Employment Security (NHES) on a pilot involving process mapping, use case analysis, and RPA development advising. This pilot helped NHES prepare to engage with an RPA vendor starting with their highest priority appeals-related use case: compiling appeals packets. Currently, merit staff work alongside clerical staff to manually compile documents into a folder and transmit it as a case file. RPA can take over this task, allowing merit staff to dedicate more time to hearings, and for clerical staff to focus on more complex tasks and customer service. Through the pilot, USDOL also shared technical assistance with NHES and collaborated to build example business requirements, bot parameters, and RPA governance, as well as other resources specific to this use case.  

Use RPA in tandem with other longer-term solutions

As states modernize their systems to adapt more quickly to changing needs within the UI process, RPA can help teams weather periods of high claim volume with more resilience while also building systems that are more responsive to staff needs.     
RPA has many advantages. Its cost effectiveness and appropriateness as a tool should be evaluated on a case-by-case basis considering the systems the state has in place already.

USDOL resources to help states evaluate and implement RPA systems

USDOL has a number of resources to assist states with implementing RPA.  

These resources include: 

Contact us

If your state is interested in learning more about how to streamline your workflows and process claims more efficiently, contact us by emailing the UI Modernization Team and CC’ing your Regional Office representatives. We can help you identify tasks that are good candidates for RPA and provide resources for implementing RPA at your agency.