Collection Scoring Model
Applying Machine Learning to Collections
Our machine learning algorithms allow accounts receivable management and collections organizations to increase collections & debt recovery through the creation of data-driven, customer specific contact strategies, dynamic scoring, and advanced analytics.
CFPB Regulations
The Consumer Financial Protection Bureau (CFPB) recently released a new rule governing the attempts to contact a consumer about a debt.
Ensuring you are able to maximize your attempts is more crucial than ever.
Read our blog post on how Machine Learning models can help with this new rule.
Debt Scoring Done Differently
Features | Traditional Fico Scoring | Out of the Box ML Scoring | The NLP Logix Approach |
Rank debt for strategic utilization | included | included | included |
100% of accounts scored | included | included | |
Successfully passed CFPB audits | included | included | |
Custom built on your data | included | ||
Dynamic score | included | ||
Flat fee pricing | included |
Prioritizing 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.
Learn more in our article Using AI/ML for Collection Scoring.
Learn More:
Today’s Challenges in the Debt Recovery Industry
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Is there a better way to handle bad debt scoring?
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