AI-Based ATM Management System
Skimming Device Detection
This feature will be able to detect presence of RFID-based skimming device installed at the ATM, by using machine learning technology.
Face & Attribute Detection
Every part of a customer’s face must be visible and not covered when he/she is using the ATM. This feature will detect the visibility of his/her face, and if he/she is detected wearing forbidden attributes such as helmet, sunglasses, facemask, or hoodie, the system will automatically display an alert to remind the customer to take off all those forbidden attributes. Customer will not be able to use the ATM until all forbidden attributes are removed.
Object & Safety Violation Detection
This feature will observe the people in the ATM’s surrounding areas and the objects they are carrying. Local alert system will be automatically triggered, whenever a suspicious behavior and/or object are detected by the system. Additionally, this feature will also be able to identify necessary activities, such as regular maintenance and cleaning.
People Counting & Dwelling Time Measurement
This feature will count the number of people queuing outside the ATM and the number of ATM visitors (total, incoming, & outgoing) in real-time. Furthermore, this feature will also be able to analyze every customer’s usage time of the ATM, and expected queuing time by calculating the average duration of both. If a customer is being inside the ATM for too long, the system will send warning alert & notification to central monitoring system.
Low Light Detection
This feature will enable the system to detect human behavior and object in low exposure (dark) condition, with computer vision and infrared technology. This will be much of help in certain cases where the lighting in the ATM is deliberately tampered, or if the ATM is located in low lighting areas, which made it prone to thievery.
Electronic Know Your Customer (E-KYC)
Optical Character Recognition
This feature will automatically recognize the data & photo in a customer’s identification card, and upload it into the respective bank’s database.
Face Recognition with Liveness Detection
This feature will serve as the facial identity verification procedure, to match the photo in the ID card with the customer’s actual face. The customer will be required to take a selfie and upload it into the system. The resemblance between the selfie & the ID card photo must be at a minimum of 75% in order to be considered a match. Liveness detection had also been added to this feature, to prevent any form of spoofing.
Jl. Taman Pinang Nikel No.35, RT.15/RW.16, Pd. Pinang, Kec. Kby. Lama, Kota Jakarta Selatan, Daerah Khusus Ibukota Jakarta 12310
Email : [email protected]
Phone : (021) 2276-8585 / 0817-17-8080-70
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