Propensity to Pay – Machine Learning Model Development Have you called, sent emails, and taken the last-ditch effort of snail mail? Is the price of debt recovery putting you in debt? How do you persuade a customer in today’s economic environment to pay their debt? Perhaps it is not about persuasion but understanding the propensity to pay. Finding a customer’s propensity to pay can offer: Likelihood of payment, taking into account Date of Service Age, Date of Placement age, Balance, Location (income, economics, education, etc.) Consumer’s preferred communication method Time of day patient prefers communication Since 2014, NLP Logix has been partnering with companies in the debt recovery industry to build machine [...]
Make your attempts count - How Machine Learning models can help with the new CFPB rule The CFPB recently released a new rule that limits contact attempts to only 7 per week per consumer. Maintaining and improving Right Party Contact (RPC) rates is now more important than ever! This means that you not only have to call the right number, but you have to do it at the right time. The rule will have many agencies scrambling to figure out a way to make the most of their calling strategies to keep their collections rates from dropping. How will agencies ensure they [...]
Despite the current financial crisis, credit scores are soaring – Is there a better way to handle bad debt scoring? For many years, credit scores have been the primary tool used to prioritize bad debt accounts for the vast majority of collections agencies. For the most part, a person’s credit score is a good indicator to determine if they are capable of paying back their debt. But with advances in AI technology and with the average consumer credit score reaching new highs, (see WSJ article here) should agencies be looking at alternative ways to prioritize their accounts? In this article, we’ll compare traditional credit scores to machine learning models as a strategy [...]
Collection Scoring - How Healthcare Providers Can Use AI/ML for Optimal Results Debt collection is a costly, yet necessary undertaking for healthcare providers. To reduce costs, debt collection often gets outsourced to a third-party agency. However, handling accounts that are more likely to pay in-house can reduce outsourcing costs. To accurately identify these accounts, healthcare providers can leverage artificial intelligence (AI) for Collection Scoring. Why Healthcare Providers Should Use AI For Collection Scoring AI-based collection scoring models allow healthcare providers to accurately determine which accounts receivable to handle in-house and which to outsource to collection agencies. This results in: Increased debt collection rates, because collectors will [...]
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.
By Ellen Schneider – Reporter, Jacksonville Business JournalNov 12, 2019, 8:26am EST Jacksonville-based artificial intelligence and machine learning firm NLP Logix announced it recently landed a new international client for its debt collection software. NLP Logix will integrate Paypensity – a product that measures the probability that a consumer will pay a debt – into Australian-based debt recovery software company CreditSoft Solutions' operations. Paypensity does a better job of measuring who will pay versus who can pay, in the way that a credit score does, said Katie Bakewell, NLP Logix's lead statistician. It provides a more holistic analysis through a machine learning approach, she added. This allows agencies reviewing large amount of delinquent accounts to prioritize and [...]