AI-Driven Mobile Business Intelligence Platform for Laundry Profit Forecasting, Anomaly Detection, and Financial Data Visualization

Small and medium-sized laundry enterprises frequently face challenges in monitoring financial performance, forecasting future revenue, and detecting operational inefficiencies. While existing mobile applications provide transaction recording and basic visualization features, they often lack intelligent analytical capabilities to support strategic decision-making. This study proposes an AI-driven mobile business intelligence platform that integrates profit monitoring, predictive analytics, anomaly detection, and interactive financial data visualization for laundry business management. The platform was developed using Flutter for cross-platform mobile deployment and Firebase for cloud-based data management. A dataset comprising 5,840 transaction records collected over a 24-month period was utilized for model training and evaluation. Data preprocessing included missing-value handling, outlier screening, normalization, and consistency validation to ensure data quality and reliability. A Long Short-Term Memory (LSTM) model was implemented to forecast future profits based on historical transaction patterns, while the Isolation Forest algorithm was employed to identify anomalous operational expenses. Experimental results demonstrate that the proposed platform achieves reliable forecasting accuracy and effective anomaly detection, enabling proactive financial management and improved business decision-making. The findings indicate that integrating artificial intelligence into mobile business intelligence systems can significantly enhance financial planning, operational transparency, and digital transformation among small and medium-sized laundry enterprises.

Mobile business intelligence, AI driven, profit forecasting

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