Creating Self-Sustaining Data & Software Systems
Data analytics in the global currency sector continues to rely heavily on traditional data mining techniques, such as SPSS and linear regression modelling. The limitation with these approaches is that they are not self-sustainable and cannot self-learn without induced supervision. Deep CNN enables unsupervised learning and thereby presents endless opportunities for our clients to install updates and upgrades in a cost-effective self-learning environment.
At iDAS Global, we take a radically different approach, employing a combination of sophisticated solutions which leverage machine vision-led tools and artificial intelligence (AI) to transform the speed and quality of data capture and analysis in real time.
We deploy key principles and methodologies developed in other scientific fields – in particular deep convolutional neural networks (CNN) – to advance the machine learning capabilities of existing systems.
More importantly, these approaches enable existing software to “self-sustain”, upgrading itself continuously over time. They also include automatic system recognition dimensions, meaning that upgrades from current suppliers are recognised and integrated no matter which software is being employed in the cash cycle.
In doing so, our solutions create a virtuous cycle of performance increases and cost reductions for Central Banks and other relevant cash industry players, without requiring major up-front spending on new infrastructure to achieve the desired results.