Deep Learning For Finance
Deep Learning For Finance : Deep learning is a part of artificial intelligence, which has produced tremendous changes in many industries.
In finance, deep learning has made outstanding contributions in many fields such as stock market forecasting, user and entity behavior analysis (UEBA), analysis of trading strategies, loan application evaluation, credit review, anti-fraud, and account leak detection. As the world of finance continues to adapt to these breakthroughs, as well as the many challenges that come with them, the space is primed to experience significant change.
AI will replace many financial professionals involved in tasks that can be easily automated.. The work of a bank credit reviewer, for example, has been greatly reduced with the support of AI or computer technology. According to OECD statistics, from 2015 to 2019, artificial intelligence has replaced about 7% of employment in the financial industry. Many experts believe that this trend will increase rapidly. The growth of AI also brings significant pressure on financial practitioners to adapt, as well as a coalition of skeptics that argue it is too early for deep learning to make an impact in finance.
Could deep learning really continue to be successful? The answer is not necessarily. There are many problems that need to be addressed to integrate deep learning into the financial industry more deeply:
Hard to define a financial problem well with a deep learning model.
The most widely used machine learning in commercialization is still supervised learning, and supervised learning requires a clear problem definition.
Taking simple supervised learning as an example, if you want to build a model to predict whether corporate mergers and acquisitions will affect the company’s stock price, companies need to provide a large amount of merger data and understand whether the stock price has changed after the merger. After collecting enough information on mergers and acquisitions and stock price changes, they would then perform natural language analysis and extract the features and put them in the machine learning model.
In actual situations, however, we cannot give a clear problem definition and boundary. If you want to use AI to formulate a stock trading strategy, it is not enough to only consider merger news. The more relevant factors, the better the fit and accuracy of the model.
For example, macroeconomic policies and specific microeconomic conditions will affect the fluctuation of stock prices. Not including one of them will negatively affect your model. In this case, each problem requires a large number of people and data to support, which is also the reason why a large number of explorations using AI to predict stock trends have not been successful.
There is a lack of effective communication between deep learning practitioners and financial practitioners.
Strong financial technology is the result of cooperation between financial institutions and technology companies. For a long time, the connection between computer and finance has been relatively weak. A machine learning expert may have a relatively superficial understanding of finance/economics, and only understand basic concepts and principles. Similarly, financial service practitioners lack the understanding of deep learning AI models. Therefore, the use of deep learning knowledge to promote the development of finance requires a large number of cross-field talents (at least one project manager who understands both directions).
The financial sector lacks sufficient big data and artificial intelligence talent reserves.
The popularity of artificial intelligence has not had time to develop a large number of professionals for the industry. A large number of AI/ML talents still tend to find jobs in more tech-focused companies, and there are not many talents left for financial service companies.
The deep learning network is still not transparent.
A neural network is like a black box. We cannot explain how it makes decisions, how to draw conclusions, or what reactions it will produce, especially if the program has never been exposed to it. There are many steps between the input and output of the algorithm that are not visible. At this point, decision trees have more advantages for business analysis than deep learning. The lack of transparency hinders the further application of deep learning in financial departments that require accountability and reliability. This connection has not yet been established in the field of artificial intelligence.
Undeniably, the new technology and data analysis methods of deep learning will disrupt the investment sector and bring efficiency and real benefits to investors. Financial institutions that have not adopted technological solutions will fall behind. Applying deep learning technology is not easy, however, and there are still many complex problems in the financial field that cannot be solved by AI.
Deep learning is just one of many tools and future machine learning methods. Can this “black box” be opened? That answer is unknown at the present, but going forward, the potential of deep learning in finance is undeniably large.
Deep Learning For Finance