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 be able to prioritize patient accounts that are more likely to pay.
- Improved liquidity, as the increased debt collection rates will translate to more liquid assets.
- More efficient use of resources, as debt collectors will be able to allocate optimum time and effort to patient accounts.
Traditional Credit Scoring vs ML-Based Collection Scoring
Traditional credit scoring models are mainly influenced by five factors, namely payment history, debt utilization, credit history, credit mix, and new credit checks. However, there are hidden factors that aren’t considered.
To also consider the hidden factors, healthcare providers can use an AI technique called machine learning (ML). Unlike traditional models, ML-based collection scoring models consider a much broader range of factors that contribute to determining an account’s likelihood of paying their debt. These ML models are trained with historical data, which allows them to identify the hidden patterns and factors overlooked by traditional collection scoring, resulting in more accurate predictions.
How ML-Based Scoring Models Work
ML-based collection scoring models use machine learning algorithms to identify patterns in previous accounts’ historical data, which are used to predict the likelihood of a patient account paying their medical debt. Since a much broader range of factors is considered with the ML model, the predictions are more reliable.
The historical data used to train the ML-based model includes attributes like account balance, debt age, and type of debt from external data sources. To train the ML model, large volumes of historical patient account data are analyzed by ML algorithms to identify distinct patterns in the input dataset. The identified patterns are then used to predict whether a new patient account is more or less likely to settle their medical debt on time.