Deep learning, or the ability of machines to learn tasks using artificial neural networks, has been around since the mid-1960s. In recent years, however, with advances in computation and storage technologies enabling the large quantities of data required for effective neural networks to be rapidly processed, the use of deep learning is increasingly being explored across multiple industries and geographies.
Artificial intelligence (AI) and deep learning may offer the potential to replace the need for human beings in certain parts of the financial services sector, bringing unprecedented innovations, efficiencies and cost savings to bear. But the successful implementation of such technologies requires unimagined innovation from humans, such that it may yet be a long time coming.
This irony at the heart of deep learning was articulately expressed by Haruhiko Kuroda, governor of the Bank of Japan, in a speech in April 2017. “If there is any risk the role of human beings is overwhelmingly replaced by AI, that would be when human beings stop thinking independently and autonomously,” he said.
“From a financial perspective, it is most important for us to think independently and positively on how to make an efficient and effective use of new technologies such as AI and big data analytics to further develop and improve financial markets and services.”
Deep learning, an offshoot of the broader family of machine learning and AI methods, is not a new concept and has existed in some form since the 1960s. It is based on the notion of learning tasks using artificial neural networks inspired by the biological nervous system. The technology is highly advanced, and requires vast volumes of data and compute power.
Machine learning methods, such as deep learning, construct predictive models from sample input extracted from large data sets. These models can provide data-driven algorithms which perform better, or are more easily constructed, than traditional programming techniques. Example applications include classification problems like e-mail spam detection or image recognition, as well as the predictive analytics such as credit score computation or financial performance.
With the rising complexity of financial markets and the increased technological sophistication of securities trading and processing, it should come as little surprise that there is now so much focus on possible use cases for deep learning. If such methods have the potential to navigate complex markets and regulations, boost profits, cut costs or bring other efficiencies to bear, they naturally merit exploration. But it remains a work in progress.
Wei Pan, co-founder and chief scientist at New York-based AI specialist Thasos Group, sees the strongest use case for deep learning in the hedge fund space. “Fundamental and quantitative hedge funds are now looking to extract information and alpha from data, and deep learning techniques can be used to do that more effectively. But there is a lot of work to be done to make the deep learning model work for the finance sector,” he says.
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