ATMs or Automated Teller Systems have become an indispensable component of banking activities. They represent the central mantle of the banking industry – always on and ready to dispense cash to serve consumers’ needs. However, ATMs, like any other electro-mechanical device, can break down at any time and put users at inconvenience. They need more attention and care with time. Technology can be the solution to deal with problems of ATM refill, maintenance, and functioning. Artificial Intelligence (AI) is increasingly used by banks in their ATM management plans. Today, AI and ATMs are helping banks operate more efficiently
AI-Based Software For ATM Management
Banks are leveraging AI to determine how individual ATMs work and the specific problems they encounter. Some ATMs get more footfalls than others based on their location. Weather and other factors may also impact its usage. That’s why banks must take into account the individual aspects of every ATM before leveraging AI for its smooth operations.
To know how well an ATM in a particular spot is running, the banks must trace back the granular details of the ATM and streamline service plans. Similar to creating a curated experience, banks must create profiles at an ATM level by considering the types and frequency of transactions. They must also take into account the fact that mobile banking is gaining traction. They must work on redesigning end-user experiences and introduce features to accommodate changing customer needs and habits.
Consistent technological advances mean there is a more efficient method available with banks to collect, store and analyze data. They can be quickly alerted to issues related to currency refill, transportation, ATM malfunctioning, etc., so that fast action is initiated to resolve the problem. AI can help detect sensor-level data that can provide more detailed information about the problem. AI can also help detect patterns in the ATM’s behavior.
The AI applications for ATMs currently used for maximizing ATM efficiency are:
- Predictive maintenance
- Machine learning for cybersecurity
- Machine vision cameras
- Projecting cash demand
AI-powered predictive maintenance software helps optimize ATM services by proactively identifying problem areas in ATMs. The software can also automatically schedule the support services needed.
The software tracks historical data from ATMs and predicts machine and component-level performance. Based on the data, banks can schedule maintenance and support activities to
Machine Learning for Cyber Security
Machine Learning can be leveraged as an internal transaction-monitoring tool for ATMs. They help curb the cyber hacking of ATMs. Rolling The security system across all ATM networks can track millions of daily transactions from multiple sources. The software can also comb through the transaction data database to provide a real-time pattern of customer behavior. Identifying suspicious activities can become easy with this system.
Machine Vision Cameras
Similarly, surveillance software systems are deployed by banks to empower AI and ATM surveillance cameras. They are designed to recognize human faces, body postures, and even objects in their field of vision. Banks also use the software to automate security for ATMs. This spares them the need to deploy security personnel, especially for ATMs in remote locations.
The software can examine the camera footage using an AI-based vision system in real time that can send alerts and create analytics for ATM security.
Projecting Cash Demand
Banks use highly advanced automatic forecasting software to test specific risk scenarios. The software can predict future demand for vital metrics such as sales and cash demand. Banks leverage the technology to enhance their ATM processes by predicting the probability of an ATM falling short of money. It proactively alerts the bank to refill ATMs.
Also, software forecast of the cash an ATM needs is based on past information. The data provides details of the frequency of use of an ATM and the amounts withdrawn from that ATM on average for a specific period. The advantage is that it can eventually reduce the instances of ATMs running out of money.
In addition, forecasting software systems can also analyze cash withdrawal data and predict cash inventory in ATMs. This data can help reduce the number of trips an employee makes to refill an ATM.
In conclusion, the use of AI for the management of various elements of financial services has increased drastically. Banks rely on AI technology to improve the management of ATMs and ensure more efficiency in their operations. Research on the subject makes it clear that predictive maintenance for ATMs ranks among the most prominent application for AI in ATM operations.
Finally, apart from improving customer experience at ATMs, the technology has also helped reduce the instances of ATMs running out of cash as it alerts banks proactively and also allows the scheduling of maintenance. Experts believe software vision systems for ATM security will grow in demand over the next few years. The demand for AI applications for ATMs is definitely on the rise. One can expect major advancements in technology in this domain in the coming decade.