Machine learning , in its simplest form, means giving computers the ability to learn and find hidden insights without being explicitly programmed to do so. Evolving from artificial intelligence (AI) and the study of pattern recognition, machine learning explores the construction of algorithms and continual optimization of patterns to make predictions on data.
We believe that AI and machine learning will improve our in-house processes (think fraud detection and call-center optimization) to deliver better experiences for our clients. It also means we can offer innovative solutions and products to our customers (like chatbots and systems that anticipate customer needs).
Machine learning is on the minds of many of the world’s hottest companies - and we’ve joined the ranks of the most forward-thinking.
In fact, five of our data team members were at at the Google Advanced Solutions Lab (ASL) and had the opportunity to rub shoulders with teams from some of the most renowned companies in the world in insurance, financial services, retail, entertainment, and stock markets.
This team spent a month at the Google headquarters immersed in the ASL program learning to solve complex, high-impact problems with the latest and greatest techniques and tools in Machine Learning.
Why are we diving deep into machine learning? ATB is becoming a disruptor in financial services. The potential for leveraging technology is as expansive as our imagination and we want to build an AI-driven banking world for ATB customers. This training was a catalyst in visioning it.
We foresee a digital banking world in which the needs of customers are anticipated real time, followed by personalized recommendations, credit applications are processed in seconds, customer service tasks are performed by a virtual assistant, and ultimately, an entirely AI-driven banking world.
The primary tools taught at ASL, Google Cloud Platform and TensorFlow, have the power to build and deploy models hosted in cloud for nearly all banking applications. Our data science team can accelerate their work through access to elastic compute engines, arrays of algorithms, and trained APIs.
After 4 weeks, 26 slide decks and 30 coding notebooks, it’s safe to say the team is positive that machine learning has a promising future for us, for you, and in the world of banking.
Want to be part of a team that is intentionally building for the future? Learn more here.