Collection Scoring Model for Debt Collectors2022-05-26T19:51:19+00:00

Collection Scoring Model

Applying Machine Learning to Collections

The NLP Logix Collection Scoring Model provides deeper insights into the Collectability of Debts

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.

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AI – Driven Collections Scoring

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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 collection scoring

Debt Scoring Done Differently

Features

Traditional Fico Scoring

Out of the Box ML Scoring

The NLP Logix Approach

Rank debt for strategic utilization

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100% of accounts scored

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Successfully passed CFPB audits

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Custom built on your data


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Dynamic score

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Flat fee pricing


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Suffering from Model Decay?

Keep your models in shape with regular check ups

Learn about Model Performance Monitoring

Interested in learning more about Collection Debt Scoring?

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medical collections

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.

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NLP Logix Partners with Receivables Management Association International

NLP Logix is proud to announce partnering with Receivables Management Association International (RMAI) through their membership program. This membership is an expression of our dedication to the receivables industry and to providing innovative technology to receivables management organizations.

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