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Healthcare Debt Collections Agency Boosts Productivity with AI
Applicable Industries
- Healthcare & Hospitals
- Retail
Use Cases
- Time Sensitive Networking
The Challenge
A leading Illinois-based collections agency, specializing in healthcare and retail receivables, was facing a significant challenge in managing their after-call work. The agency wanted to minimize the time their agents spent on after-call tasks and increase their in-call productive time. The goal was to enable their agents to work on more accounts and focus more on talking to borrowers, rather than getting distracted by other tasks. The agency was looking for a solution that could help them streamline their operations, increase productivity, and ultimately, improve their bottom line.
About The Customer
The customer in this case study is a leading collections agency based in Illinois. They specialize in healthcare and retail receivables, working with a range of clients to manage their debt collections. The agency's primary goal was to increase the productivity of their agents by reducing the time spent on after-call tasks and increasing in-call time. They sought a solution that would allow their agents to focus more on talking to borrowers and less on administrative tasks, ultimately enabling them to work on more accounts and improve the agency's overall performance.
The Solution
To address this challenge, the agency turned to Prodigal ProNotes, a solution that leverages speech and Natural Language Processing (NLP)-based AI technology. Prodigal’s proprietary AI engine automatically summarizes debtor conversations into succinct notes in real-time. These auto-generated notes replace the manual notes that collectors would typically take during the after-call wrap. The note is generated as soon as a call ends, giving collectors the flexibility to review and edit it before submitting. This solution not only reduced the time spent on after-call tasks but also improved the efficiency and productivity of the agents.
Operational Impact
Quantitative Benefit
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