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.