Effective water pricing and timely bill collection are essential to prevent resource waste and ensure the financial stability of water and wastewater companies. This study analyses the spatial distribution and aging patterns of outstanding receivables in Shush and Dasht-e Azadegan, Khuzestan Province, using GIS-based spatial data mining techniques implemented in ArcGIS 10.8.1 in order to support more effective, data-driven debt management strategies. A dataset of over 90,800 water subscriptions, collected over a six-month period, was used to integrate spatial and financial data. Results indicate that 77.08% of outstanding receivables are of relatively low financial value (less than 10 million Iranian Rials), whereas 22.92% of subscribers carry heavy and high-risk debts. Optimized Hotspot Analysis identified statistically significant clusters (p < 0.05) of high-value and aged debts in Susangerd, Abu-Homeizeh, and Kut- Seyed Naeem, highlighting localized financial risks. In contrast, Shush and Bostan emerged as cold spots (p < 0.05), reflecting lower concentrations of overdue receivables. Overall, receivables aging analysis revealed that 54.19% of debts are recent (one year or less), with the highest short-term debt concentrations in Shush (67.25%) and Bostan (56.49%), both showing significant spatial clustering (p < 0.05). Although Alvan reported 64.72% of debts as short-term, the limited presence of cold spots suggests that favorable age profiles do not necessarily translate into spatially coherent financial stability. However, several cities exhibited disproportionately high levels of long-term debts, reflecting persistent inefficiencies in collection practices. These results underscore the importance of integrating spatial and temporal analyses to support targeted, data-driven receivables management strategies.