Sold by: NLP Logix
Maximize your collection attempts with Machine Learning powered Collection Scoring.
Overview
AI - Driven Collection Scoring Model
Our machine learning algorithms allow accounts receivable management and collections organizations to increase collections and debt recovery through the creation of data-driven, customer specific contact strategies, dynamic scoring, and advanced analytics. Debt Collection Scoring done differently:
- Rank debt for strategic utilization
- 100% of accounts scored
- Successfully pass CFPB audits
- Custom built on your data
- Dynamic scoring
- Flat fee pricing
Prioritize your collection accounts for outsourcing Healthcare Providers typically outsource their receivables to enable their staff to focus on patient care rather than collections. With our scoring model, you can easily prioritize the collection accounts that will be more likely to pay, saving on outsourcing fees.
Highlights
- Why use an A.I. company for your collection scoring? We are able to take a deeper dive into your data to produce better results. Additionally, we can easily incorporate additional automation and AI-Driven data capture to create a full program to reduce your need for human interaction.
- Data Science is a Team Sport.® It's not just our mantra, it's how we do business. Let's work together to find the right solution for your business.
Details
Pricing
Custom pricing options
Pricing is based on your specific requirements and eligibility. To get a custom quote for your needs, request a private offer.
How can we make this page better?
We'd like to hear your feedback and ideas on how to improve this page.
Legal
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
Support
Vendor support
Not sure where to start? We are happy to help.
Support inquiries can be sent to:
- email: contact@nlplogix.com
- phone: 904-208-5065
- webform: