When electricity was invented more than a hundred years ago, it revolutionized society; suddenly people had the exciting, yet challenging task of adapting to a new technology, and a new way of living. Industry was transformed, life was revamped and everything seemed newly possible. Today AI puts us in a very similar position. Just as it is very difficult to imagine households, communications, healthcare and practically every domain of our life without electricity nowadays, in recent years AI has advanced to a point where we can see change of a similar magnitude happening in the not-so-distant future. A change set to refashion industry yet again and mark a turning point in history.
The rise of financial technology, or FinTech, has been one of the most exciting developments of recent years. Firms such as Transferwise, Kantox, Monzo and iZettle have proved that tech startups are capable of offering individuals and SMEs quicker, cheaper and more convenient financial services than traditional banks.
New progress in the area of artificial intelligence and machine learning has created the opportunity for fintech to steal yet another march on banks. Creative application of machine learning has the potential to enable startups to also offer a fairer and more personal service than anything that has come before.
ON THE AI FUTURE OF FINANCIAL SERVICES
At Strands we're stacked full of FinTech experts of all stripes and colours, but did you know we have two in-house gurus on artificial intelligence and machine learning of our very own?
In the lead-up to the D-RAFT AI & Robotics Demo Day, we sat down with each of them to pick their brains about the future of AI and ML with a special banking twist. Thanks to the folks at Prowly for developing these incisive questions.
Before we get started with the interview, some introductions are in order...
Machine learning has had fruitful applications in finance well before the advent of mobile banking apps, proficient chat bots, or search engines. Given high volume, accurate historical records, and quantitative nature of the finance world, few industries are better suited for artificial intelligence. There are more uses cases of machine learning in finance than ever before, a trend perpetuated by more accessible computing power and more accessible machine learning tools (such as Google’s Tensorflow).
Banks must move from a product-based paradigm to one that puts the customer in the driver’s seat.
Banks today are grappling with an overly simplistic understanding of their customers combined with a vastly complex product set with only very subtle differences, frequently unappreciated by customers. All of this comes at a significant cost in terms of operations, technology, and service.