The Guarantee Trusted Bank (GT Bank) at Opera Square, Accra, currently faces challenges in accurately counting and monitoring customers entering and exiting the bank premises, leading to operational inefficiencies and safety concerns. Additionally, the current system for counting and monitoring customers through both a CCTV and physical approach in the bank is prone to errors and cannot collect and store data for future planning. Security personnel also lack knowledge of the exact number of daily visitors and struggle to determine the exact timing of incidents during working and closing hours. This study aims to explore the weaknesses of the current detection and counting system at the GT bank and proposes solutions to address these issues. The primary solution involves the implementation of a computerized human detection and counting system, designed to accurately detect and count individuals who enter and exit the GT bank’s branch premises. To achieve this goal, a qualitative methodology is employed. Our qualitative method involved interviewing participants using relevant open-ended questions, which motivated them to express their thoughts and views openly with no limitations. The analysis of quantitative data illustrates the difficulties faced by the current system in detecting and counting customers in the bank. Furthermore, the research participants are willing to accept our proposed system, which would improve the current counting and monitoring system used by the bank. Our proposed system utilizes computer vision and deep learning algorithms through the use of a single-shot multi-box detector (SSD) to identify human figures in video feeds. By introducing the proposed system within the GT bank, it can improve the overall customer experience. One implication for decision-makers involves the designation of specialized personnel responsible for system maintenance and troubleshooting, thereby assisting in routine checks and upkeep to prevent system downtime and maintain continuous operation.