AI is the Future

Since the origin of modern banking, there have been many transformational upgrades to banking systems. The demands of banking have accelerated technology developments and in turn banking has been quick to adopt new technologies. Machines have played an ever increasing part in banking.

As the era of intelligent machines dawns, banking is poised for another deeply transformative period. With Deep Learning, Banks can now imagine complex ‘intelligent’ systems that deliver results in real life beyond the research labs of academia.

Before Deep Learning, ‘AI’ systems were built on rule based systems and had limited success. Deep Learning has changed the complete landscape of ‘AI’ over the past three years. The success of Deep Learning has gone beyond just recognizing images and identifying speech. Many industries are now looking at Deep Learning as the key strategic technology differentiator, that can enhance their delivery and operational efficiencies.

Delivering the best possible user experience to banking customers in an efficient and cost optimal manner has become a strategic goal. Hence, intelligent automation has become the point of interest to improve performance, reduce human effort and load and be cost efficient.

With access to large quantum of training data in Banking, Deep Learning based automation is a great opportunity. Unlike simple rule based automations, systems using Deep Learning can automate processes involving multiple data formats like text, images, numbers etc. in executing a task.

The larger impact of ‘intelligence’ is when it assists humans in performing the same set of tasks at much faster speed with better efficiency. Increase in complexity of data and complex inter dependencies among tasks have meant that professionals are often overwhelmed and unable to process information and respond suitably.

‘Artificial Intelligence’ can help professional – to make long range inferences and take step wise decisions for a bigger outcome. In ‘Banks’, these assistive systems can transform the fundamental processes and re-invent new processes embedded with ‘intelligence’ driven decisions.

These ’Assistive Systems’ can be applied at every phase of human interaction for broad goals like – reduce in processing time, increase in operational efficiency, financial & economic advantages.

Compared to other industries, banking has been an early and extensive adopter of analytics. Banks use analytics to make smarter decisions and adapt to anticipated changes. Banking industry produces vast quantum of data of all varieties – text, images, numbers, videos.

With growing volumes of data, Banking industry had been investing in multiple technologies at each phase. Among other technologies, Deep Learning is uniquely suited to processing different data types and vast volumes to build multifunctional intelligence. Banks can use Deep Learning to understand customer needs more accurately and serve them. DL can also be used to supplement traditional analytics to arrive at a richer picture of historical analysis as well as predictive scenarios.

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