A merchant analytics framework for revenue forecasting and financial stress detection using transaction data - Harb, Yara; Baaklini, Wissam; Abbas, Nadine; Kadry, Seifedine (IAES International Journal of Artificial Intelligence (IJ-AI), 2025)
By processing payments and providing specialized financial services, acquiring banks are essential for merchants’ operations. To forecast 30-day revenue trajectories, identify seasonal demand patterns, and identify early indicators of financial stress, this paper presents a scalable merchant analytics framework that benefits from transactional data. The framework captures multi-level seasonalities using Prophet time series model, allowing dynamic product offerings like revenue-based loans. Proactive risk management is supported offerings like revenue-based loans. Proactive risk management is supported. by a new stress-flagging mechanism that identifies merchants at risk based on deviations in revenue trends. The framework achieved a median 30-day mean absolute percentage error (MAPE) of 56.51% after the validation on a dataset with 130,350 transactions from 460 merchants in a volatile economic environment. The model demonstrated significant practical utility in identifying financial distress and segmenting merchant behavior, despite its moderate predictive precision, which is common challenge in high-variance merchant datasets. Model outputs are converted into decision-support visualizations along with an interactive dashboard.