Eliminating Big Data Management Costs & Inefficiencies through AI
The application of machine visioning technologies in banknote production, fitness and inspection can generate a huge amount of data for Central Banks, cash centres and other suppliers to handle – regardless of the touchpoint in the overall cash life cycle.
The sheer size and complexity of this information can affect the speed and efficiency of an organisation’s data processing, capture, migration and storage systems. This is particularly true when a single or double loop approach is employed in a supervised space.
As a result, IDAS Global has pioneered a set of deductive-inductive approaches, leveraging artificial intelligence (AI) technologies, which significantly reduce both inefficiencies and operating costs in big data management.
We employ Deep Convolutional Neural Network (CNN) methods which create self-sustaining, supervised and unsupervised systems. Our approach condenses massive data quantities into byte-size parcels that do not occupy operational memory space, thereby radically boosting performance. The application of other AI and machine learning tools delivers a ‘smart’ system which enables, data capture, migration and analyses data at an unprecedented rate, and also constantly upgrades itself free from extensive human supervision