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.
AI is the theory and development of computer systems able to perform tasks previously thought to require human intelligence. Broken down, this is a software consisting of a family of algorithms which helps our machines to learn. Machine Learning (ML) is the procedure of providing our machines with large amounts of data and letting them learn by themselves, inspired by the way that we, humans, learn. ML algorithms repeatedly process available data, so that they can make accurate predictions or classifications.
We are all becoming increasingly familiar with lucrative applications of AI such as speech recognition, machine translation or even self-driving cars. Current research on AI now focuses on the next steps of these applications. For instance, AI is on course to produce well-trained models that will be able to read and understand large volumes of text. They will be able to read, summarize and answer questions accurately, reaching almost human levels of interaction and performance. Whilst these huge strides in AI will become the new normal in a relatively short time, as a consequence of these rapid advances, companies in every sector and industry are faced with the challenge to stay agile as they navigate and adapt to this modern reality.
Bots Over Brain Power?
Traditionally every company has a development team, which receives some input data sets with the corresponding output results. The team analyzes the data, designs algorithms based on a prefixed set of rules and then tests the outcome of this model against the given outputs. In the new era of ML, we create algorithms that are able to learn the rules that govern the relations between the given inputs and outputs (labels). The ML algorithms are not given any directions or constraints. They are fed with the available data and they independently develop the ability, using mathematics, to detect patterns and make the best possible approximation of input–output data mapping.
Therefore, without human aid or interference, our machines are able to handle vast amounts of data and learn how to map inputs to outputs quickly and efficiently. A basic example of how ML models work:
In a data set that contains labeled images of both lemons and apples, an ML algorithm can be trained to distinguish a picture of a lemon from that of an apple. In the process of training, we don’t give the expressed instruction to the algorithm to look mainly for round, red fruits, in the case of apples. The algorithm itself realizes that shape and colour are two very important features for the classification task that it has to perform. In many tasks the performance of a trained ML model surpasses human-level performance.
AI drives incredible amounts of economic value. Almost all of the economic benefit created by AI today is through the above learning process based on input–output data. Within banking, one example would be in relation to loan approvals, an area where banks need as much information as they can get about creditworthiness before signing on the dotted line.
Machine Learning algorithms get fed with data describing client characteristics and outputs informing if these clients are able to pay their debts or not. In these cases, machines learn from financial history and become experts in evaluating new loan applications. In online advertising, ML algorithms are provided with records of consumers habits, so they easily identify customers with similar preferences and make appropriate suggestions. A prime example would be the Amazon recommendation system. This allows companies to build ”intimate” relations with their customers.
For success stories using ML, two examples would be Man Group and Bank of America. Man Group Plc. built a system that evolved autonomously, finding money-making strategies humans had missed. The results were startlingly good. By 2015, Artificial Intelligence was contributing roughly half the profits of one of Man Group’s biggest funds. Consumers today are becoming increasingly accustomed to the types of seamless mobile experiences provided by apps like Uber and Airbnb and want better banking experiences. To this end, Bank of America introduced Erica, the new digital assistant. Erica uses Artificial Intelligence, predictive analytics and cognitive messaging to help customers to make payments, check balances, save money and pay down debt. As systems like Erica become second nature and an intrinsic part of our lives, it won’t be long before fully synchronized voice banking, payments and commerce are as commonplace as cashing a check.
Machine Learning and Artificial Intelligence: The electricity driving banking into the future.
ABOUT THE AUTHOR
DR. DIMITRIOS VLITAS
Head of Data Science at Strands, Dimitrios has more than 17 years' experience in the field of Mathematics and Computer Science and is Visiting Professor of Mathematics and Machine Learning at the University of Toronto.