1 S Sudharshan, 2 G Raghavi, 3 Mamatha, 4 P Abhishek, 5 Sk Mufazal 03 09 The Bank Branch Performance Comparison Dashboard is a data analytics and visualization system designed to monitor and compare the operational and business performance of different bank branches Banks operate through multiple branches that may differ in customer volume deposits loans transactions revenue and service performance Analyzing these indicators helps management understand branchlevel performance and identify areas that require improvement The proposed system collects appropriate branchlevel information such as branch ID location customer count deposits loans transactions account openings revenue operating expenses and service metrics The system processes this information and calculates important Key Performance Indicators KPIs such as total customers deposit growth loan volume transaction volume revenue profit contribution and customerservice performance The dashboard presents branch performance through interactive charts graphs tables maps and KPI cards Bank managers can compare branches based on deposits loans customer acquisition transactions revenue and other approved business indicators Filters allow users to analyze performance according to region branch type reporting period and business category The system can identify highperforming and underperforming branches by comparing their KPIs and historical trends Managers can observe changes in branch performance over time and identify differences between regions or branch categories These insights can support resource allocation branch planning customerservice improvement and business strategy Overall the Bank Branch Performance Comparison Dashboard provides a centralized platform for transforming branchlevel banking data into meaningful management insights It reduces manual reporting efforts and supports datadriven decisionmaking Future enhancements can include AIbased performance forecasting branch benchmarking anomaly detection customer segmentation profitability prediction and intelligent resourceallocation recommendations Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026