Suspicious Behavior Detection Systems in Banks: Complete Development Guide

Nearly 39% of suspicious activity reports (SARs) banks file are related to fraud. This creates an urgent need for banks to upgrade their security infrastructure to ensure the safety of both digital and physical banking environments. 

The term “fraud” not only highlights identity theft, account takeovers, and data breaches, but it also includes physical security breaches such as unauthorized access, aggressive behavior, and loitering inside bank premises. While most banks are already using cybersecurity software for fraud detection and prevention, it’s the physical security breaches that require attention. 

That’s where “AI-powered suspicious behavior detection systems for banks” come into play. These systems blend with traditional security and surveillance devices to identify suspicious human behavior and alert personnel in real-time. Isn’t it amazing? 

A suspicious behavior detection system can be used for both cyber/financial transactions monitoring and physical surveillance. However, in this blog, we will dig deeper into the physical aspect of suspicious activity detection.  

From use cases, technologies, and benefits to the step-by-step process for creating an AI-based suspicious behavior detection system for banks, we will cover everything in detail. So, without further ado, let’s start. 

What are Suspicious Behavior Detection Systems

As briefly mentioned above, an intelligent behavioral detection system is an AI-powered system that can monitor and analyze human behavior in real-time. It utilizes technologies like computer vision, machine learning, and deep learning to detect harmful or suspicious behavior, loitering, sudden running, aggression, unauthorized access, unusual crowding, and abandoned objects in a bank’s premises. 

Why is a Suspicious Behavior Detection System Critical for Modern Banking

Below are some reasons why banks should implement an intelligent behavior analysis system:

#1 Increasing Number of Physical Threats 

Like cyber threats, the number of threats or crimes, like armed robberies, vandalism, and unauthorized access, in brick-and-mortar bank spaces is also increasing. The FBI reported around 1300 bank robberies, burglaries, and larcenies in banks in 2023. An AI-based behavior detection system can help prevent such incidents by identifying aggression, suspicious activities, and loitering in real-time. 

#2 Maintaining Regulatory Compliance is Critical

Like every financial institution, banks are also expected to follow physical security protocols defined by local and international regulations. Intelligent behavior analysis systems can help banking organizations to meet these requirements associated with incident reporting, access control, and video surveillance, without additional effort. 

#3 Ensuring Customer and Staff Safety is a Must

For a bank, it is crucial to create a secure environment where customers and personnel can feel safe. This also paves the way for a long-term relationship with customers. With real-time monitoring and threat detection, banks can protect their employees and customers from unexpected risks. 

#4 Proactive Risk Mitigation

Traditional security and surveillance systems can only record what’s happening or who’s coming in or going out of the banks. AI-powered behavior detection systems can immediately detect unusual or suspicious behavior to alert authorities. Security personnel can quickly intervene to prevent unwanted incidents that may result in financial losses and also damage a bank’s reputation.

Traditional Surveillance Vs. Surveillance Powered by a Suspicious Behavior Detection System

Feature/Aspect	Traditional Surveillance	Suspicious Behavior Detection Systems
Monitoring Method	Monitoring by human security personnel	AI-based real-time monitoring and analysis
Detection Capability	Passive recording, no detection	Automatically detects abnormal/suspicious behavior
Response Time	Totally depends on human observation and reaction	Instant alerts based on predefined behavioral models
Scalability	Scaling requires hiring more resources	Easily scalable across multiple locations
Accuracy	May involve errors in the output	The error rate is quite low with AI models that self-train over time.

Use Cases of Suspicious Behavior Detection Systems for Banks 

A suspicious activity detection system revolutionizes a bank’s security infrastructure by loitering detection, aggressive behavior detection, crowd gathering analysis, and in many other ways that have been explained below:

#1 Loitering Detection 

The system can monitor ATM lobbies, bank entry points, and teller zones to analyze the duration for individuals. Upon finding someone lingering beyond a predefined threshold without being engaged or indulged in any activity or process, the system immediately marks it as “suspicious behavior.”

For instance, if the suspicious activity detection software, integrated with a bank’s CCTV surveillance system, finds someone standing outside an ATM for over 20 minutes without using the machine for cash withdrawal or information, it may trigger an alert to the security guard to intervene. 

#2 Unauthorized Access Detection

An SBDS can be used to detect instances where an unauthorized person tries to access a restricted area or place, such as a server room or a vault. They might do this by closely following authorized individuals and quickly entering the place without scanning any credentials. 

The suspicious behavior detection system can help avoid this by using person counting and access verification techniques. It can even raise an alert to ensure a rapid response by security personnel. 

#3 Aggressive Behavior Detection

This is one of the best applications of a suspicious behavior detection system. Such a system is capable of analyzing body movements, posture, and even the voice tone to detect signs of aggressive behavior. The behavior could be shouting, hitting, or other similar gestures. As soon as the system detects it, it raises an alarm to alert authorities to intervene and take action to stop violence. 

#4 People Counting During Off-Hours

One of the use cases of suspicious behavior detection systems is to track the number of people around the bank, ATM, or any other restricted areas. Upon detecting any suspicious behavior, it sends immediate or real-time alerts to security alerts to the security professionals. 

For instance, if the system detects a sudden movement or gathering of more than 4 individuals around the security vault, it will raise an alarm. 

#5 Masked Individual Detection 

The intelligent behavior detection system can be used to identify individuals wearing masks inside the bank premises. The system uses face detection algorithms to determine whether it seems excessive or suspicious, alerting the security teams. 

For instance, an individual entering the bank with a full mask or monkey cap and wearing dark glasses can be detected as suspicious by the system.

#6 Abandoned Object Detection

The system can be used to detect a bag, purse, parcel, or any other object that is left unattended in restricted areas or within the bank premises. Security personnel can quickly intervene to prevent potential threats like theft, bomb blasts, and others. 

For instance, a customer entering the branch, leaves their bag near the inquiry counter, and doesn’t come back even after 15 minutes, can make the system raise an alert for further investigation. 

Benefits of Implementing an Intelligent Behavior Detection System 

An AI-enabled system capable of detecting abnormal or suspicious behavior can level up the security of the bank staff and customers, reduce surveillance errors, ensure a faster response to incidents, and facilitate data-driven incident investigations:

#1 Strengthens Branch Security

Implementing an AI-powered behavior analysis solution gives banks peace of mind about the safety and security of their personnel as well as customers. The system adds an additional layer of security by actively monitoring and identifying suspicious activities in real-time. These systems can also raise an immediate alarm to the staff in case of any unusual activity. 

#2 Minimizes Surveillance Errors

Conventional surveillance relies completely on human monitoring and analysis. Such systems are capable of only recording video footage, which security personnel analyze by spending hours. The chances of missed threats due to distractions, oversight, or fatigue are common. AI-powered surveillance automates monitoring and analysis processes and ensures no critical event goes unnoticed. Moreover, they ensure complete accuracy in the output. 

#3 Faster Response to Incidents

By generating alerts as soon as they detect suspicious activity (unauthorized access, a person carrying a weapon, abnormal behavior of staff or customers, unusual crowd in the bank), AI-powered suspicious activity detection systems can prevent losses.

#4 Data-Driven Incident Investigation 

In case of a security breach or incident like robbery, burglary, and others, a smart suspicious behavior detection software can save time and ensure accurate investigation. The system indexes and tags video feeds based on events. Investigating teams can quickly go through the feed to get concrete information.

Technologies Behind a Suspicious Behavior Detection System

An intelligent behavior analysis system utilizes multiple technologies to detect behavior and provide smart surveillance that strengthens a bank’s security posture. Explore more about the backbone technologies of such a system:

Computer Vision 

Computer vision enables the behavior detection system to accurately interpret visual data from surveillance cameras to identify and track people, objects, and actions in real-time. The systems powered by this technology can detect movement, posture, spatial behavior, and facial features and expressions. 

Machine Learning

With machine learning, behavior analysis solutions get self-learning and improvement capabilities. ML algorithms make the solution/system capable of learning from historical data and behavior patterns so that it can differentiate between normal and suspicious behavior or activity. 

Deep Learning

Deep learning (mainly the convolutional neural networks) gives smart activity detection systems the capability to do in-depth image and video analysis for facial recognition, aggression detection, and masked face identification.

Read More: 

Behavior Analytics 

With this technology, intelligent behavior analysis systems learn to understand normal behavior in ATM lobbies, security vaults, and other areas where a bank operates. It also understands flag deviations that could be a potential threat. 

Edge AI

Edge AI increases the speed at which the suspicious behavior analysis system delivers insights. The technology makes it possible to process data right where it is generated, i.e., the edge servers near the CCTV/IP camera. 

IoT

SBDS connects with IoT devices, like access control systems, motion detectors, and smart alarms, to quickly raise an alert or even impose lockdowns upon detecting suspicious behavior or activity. 

Cloud Computing 

This technology is particularly useful in large-scale deployments or large bank premises where the daily footfall is high. These platforms facilitate centralized data storage and remote access with the utmost security and scalability. 

Suspicious Behavior Detection System Development Process 

Building a suspicious behavior detection system is not child’s play. It requires in-depth expertise in technologies like AI, ML, computer vision, deep learning, edge AI, and cloud computing, along with an understanding of the security-related challenges of a bank. These are the steps that need to be followed for suspicious behavior detection system development:

An alternative approach is to partner with an AI video analytics solution development company that has prior experience in building suspicious behavior detection systems or developing AI-powered software for the banking industry. 

#1 Analyzing Requirements 

The first step to suspicious behavior detection system development involves identifying the challenges or areas where this system is required. This could be a bank branch, ATM, security vaults, or server rooms. 

Once done, define the specific behavior for which you wish to build the system. The behavior could be loitering, tailgating, entering a restricted area, aggression, etc. Lastly, mention the software or surveillance system (CCTV camera, alarm systems, etc.) with which you want to integrate the behavior analysis system. 

#2 Collecting and Preparing Data

Collect surveillance footage from the bank’s existing CCTV cameras and real-world data. Begin the data annotation process to label the behaviors shown in the footage. The prepared data will be used for AI model training. Therefore, don’t forget to ensure diversity in data collection with different lighting, angles, and crowd density. 

#3 AI Model Development and Training

The next step is to create an AI model and train it on the prepared datasets. You can also choose from existing AI models and train them using various techniques like pose estimation, object tracking, anomaly detection, behavior detection, and more. Make sure you verify mode accuracy with test footage.

#4 System Integration

Integrate the AI models within the bank’s existing camera infrastructure and security/surveillance systems. Ensure real-time video stream analysis and alert generation. Remember to ensure its capability with the video management system. 

#5 Testing 

Testing is a crucial phase of any app or system development. Therefore, check the system for detection accuracy, make changes to the sensitivity levels to minimize false output, and refine the AI models based on feedback received or the results generated after testing.  

#6 Deployment and Maintenance

Deploy the suspicious activity detection system across selected areas or bank locations. Verify that it is performing as expected. Configure dashboards, alert protocols, and access controls for assigned personnel. Continuously monitor the system for performance, update AI models on new data, and ensure regular support to fix bugs and update new features.

Also Read: AI-Powered Video Analytics for Public Sector

How Quytech Can Help

Quytech is an experienced AI video analytics solutions development company that builds top-notch AI camera solutions and suspicious behavior detection systems to level up security and surveillance in banks and other industries. We have dedicated experts with proficiency in artificial intelligence, computer vision, machine learning, behavior modelling, edge AI, and deep learning. 

We develop and deliver customized and scalable AI surveillance solutions to align with the unique security requirements of a bank. Apart from suspicious behavior detection systems, we have built AI-powered facial recognition, car damage detection, object recognition, and other similar solutions using emerging technologies. 

Final Words

With the increasing number of cyber threats and physical security breaches, adopting a suspicious behavior detection system is no longer a choice for modern banks. Powered by AI and video analytics, these systems bolster security by detecting unusual behavior, unauthorized access, and abandoned objects to enable banks to adopt a proactive approach to protect people, assets, and reputation. 

Give this blog a read to know more about intelligent behavior analytics systems, including their benefits, step-by-step development process, and technologies that play a pivotal role in these systems’ functioning. 

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