Charles Elkan, global head of machine learning at Goldman Sachs, is focused on driving machine learning and artificial intelligence strategy for the firm, and on applying leading techniques to commercial opportunities.
Charles answers some common questions and dispels some misconceptions about artificial intelligence.
How can financial services firms benefit from AI, and how is Goldman Sachs applying it to their businesses?
Charles Elkan: It’s an exciting time to be involved in AI applications, specifically as we look at how to apply automation to the world of financial services. AI can help improve speed, access and reliability across a number of businesses, from a customer applying for a personal loan online to a client receiving automatic notifications when they might want to divest from a particular industry or stock.
Where do you see the biggest advantages?
CE: I see the biggest potential for financial services firms to benefit in three areas: (1) making trading decisions, for example whether to close a position quickly or to be willing to keep it open; (2) making investment decisions, for example picking stocks for a mutual fund to invest in, and; (3) improving conversion for retail customers, for example increasing the number of visitors to the Marcus website who open a savings account with us.
How can AI be implemented now and what will future applications look like for banks?
CE: Research breakthroughs in the 1980s enabled high-value applications such as automatic processing of hand-written checks. Now, breakthroughs in the 2010s have enabled even more impressive applications, such as natural language translation and voice recognition. However, AI involves two types of understanding: shallow and deep. Shallow understanding is the ability to answer questions of just one type, such as counting the number of faces in a photograph. Deep understanding is the ability to answer questions using human-level background knowledge, such as why a person in a photograph might be feeling happy or embarrassed. Currently, AI methods are only capable of shallow understanding, so applications that need human-level deep understanding are really not realistic. There is no law of nature that says deep understanding is intrinsically impossible, but new advances in basic scientific research will be needed, and these advances are not predictable.
How much further can AI realistically develop to meet the needs of financial firms?
CE: Financial firms are using language translation and voice recognition for business applications that are similar to those in other types of companies, such as customer service. At Goldman Sachs, and in the world as a whole, we are still in the early stages of applications of deep learning.
There is enormous room for new and improved applications using the current generation of AI methods. For example, there are pre-trained models for recognizing objects in images, nonlinear forecasting methods, and much more. Another example is the detailed forecasting of volume for trading in many different instruments. This is an application that is feasible today, but that we haven’t yet built everywhere that it can be leveraged commercially.
Humans are using AI today for translation and scanning of enormous quantities of data, which is allowing us to consume data at a rate the human mind just isn’t capable of. However, humans are still an essential part of the process – we cannot rely on the computer to interpret what this data means.