Real-Time Early-Default Detection Using Streaming Machine Learning with Multi-Source Behavioral Signals

This work presents a real-time default-detection model integrating streaming behavioral signals, including app usage dynamics, repayment timing, transaction irregularities, and short-term income proxies. The model is built on 2.7 million active loan accounts with second-level event streams. A streaming-enabled ensemble classifier (online gradient boosting + incremental random forest) is deployed with a sliding window of 7 days. The model predicts impending 30-day delinquency with an ROC-AUC of 0.89 and reduces detection delay by 9.2 days on average. Incorporating real-time behavioral drift scores improves early-warning accuracy by 24.4%. The system demonstrates the feasibility of continuous credit-risk monitoring using high-velocity behavioral data.