Ai-driven Banking Security Monitoring System

• Author(s):

  Dr. P. Pavalakodi | Pravin Raj X | Rohith kumar M | Sabarinathan C | Sameerudeen M

• Keywords:

  Fraud Detection, LSTM Networks, Federated Learning, Privacy-Preserving, Deep Learning

• Abstract:

  Banking Environments Consistently Rank Among The Most Security-critical Operational Domains, Where Institutions Must Safeguard Financial Assets, Sensitive Customer Information, And Personnel Against Theft, Fraud, And Unauthorized Access. Employees And Customers Operate Within Dynamic Indoor Spaces Where Incidents Such As Suspicious Movement, Identity Concealment, Or Unauthorized Entry May Occur Undetected, Especially Under Low Lighting, Occlusions, And Crowded Conditions That Conventional Surveillance Systems Cannot Reliably Analyse In Real Time. Existing Solutions — Passive CCTV Monitoring, Rule-based Motion Detection, And Continuous Manual Supervision — Each Fail To Deliver Autonomous, Accurate, And Real-time Threat Detection Across Complex And High-traffic Banking Environments. This Paper Introduces AI-Bank Secure, A Vision-based Anomaly Detection And Facial Recognition Framework Purpose-built To Address These Limitations. The System Continuously Processes Surveillance Camera Video Through A Motion Vector-based Analysis Pipeline Integrated With A Gaussian Mixture Model (GMM), Extracting Foreground Segmentation, Object Motion Patterns, And Behavioural Deviations To Identify Suspicious Activities. A Tracking Module Employing Blob Analysis And Inter-frame Object Association Reliably Monitors Movement Trajectories And Distinguishes Abnormal Behaviours Such As Loitering Or Sudden Directional Changes From Normal Customer Activity. Simultaneously, An ArcFace-based Deep Learning Model Generates Discriminative Facial Embeddings To Perform Real-time Identity Verification Against A Registered Database.Upon Confirmed Detection Of Anomalous Behaviour Or Unidentified Individuals, Multi-channel Security Alerts Are Dispatched Immediately, Including Captured Evidence Frames, System Notifications, And Automated Messages To Security Personnel — All Without Human Intervention. The System Operates Effectively In Low-light And Crowded Indoor Conditions Through Adaptive Preprocessing, Requires No Wearable Devices, And Maintains A Structured Incident Log For Auditing And Analysis, Representing A Substantial Advancement Toward Improving Real-time Threat Detection And Operational Security In Modern Banking Environments.

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Other Details

• Paper id:

  IJSARTV12I4105192

• Published in:

  Volume: 12 Issue: 4 April 2026

• Publication Date:

  2026-04-29

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