Artificial Intelligence In Financial Services
Artificial Intelligence In Financial Services :
Application and Challenge of AI in Financial Service
Artificial intelligence is not a new topic — it has developed for more than half a century. However, it was not until the last decade when artificial intelligence began to develop rapidly and became widely used. This is due to several reasons, first and foremost being the recent data explosion in society.
With the advent of the Internet, especially the era of mobile internet, more and more people and enterprises were connected to the Internet, which generated tremendous structured and unstructured data, such as consumption records, credit records, social media information, etc.
Improvement in computing power. As stated by Moore’s Law, the computing power of computers has experienced exponential growth in the past 20 years.
Today’s computers are able to process tens of millions or even billions of data. This also provides the foundation for commercial applications of deep neural networks such as CNN and NLP. The application of AI in finance In the field of financial services, the research and application of artificial intelligence has been going on for 10 years.
At present, AI algorithms are used mostly for Credit rating and fraud detection. Since Banks have a large amount of structured data such as financial data of borrowers, consumption records and default records, there exists significant potential for macroeconomic indicators.
Better performance can be achieved by simple classification algorithms such as logistic regression and binary trees.
NLP identifies unstructured data such as Twitter and other social media sites that generate more than one billion pieces of data per day, and many of these tweets have very close links to public companies or stock markets.
The NLP (nature language process) algorithm can automatically identify these unstructured data, quickly capture changes in market information, and obtain excess returns.
In addition, the NLP algorithm is also used to automatically identify corporate financial statements and automatically convert unstructured data into structured data.
Asset pricing offers an arena for significant improvement with the implementation of Ai, reinforcement learning can be used to optimize existing asset pricing models. For example, with option pricing, the BSM model is the most classic and most widely used model.
Nowadays, using the reinforcement learning method to optimize the option’s replicated portfolio can effectively reduce the hedging error. Related research has just begun. At present, there are few models such as QLBS (Q-learner in Black Scholes) model proposed by Igor Halperin trying to solve this problem.
The challenge of AI in Finance At present, the application of artificial intelligence in the field of financial services, especially in trading strategies, is not widely used. Most banks and investment banks are very cautious about artificial intelligence, they mainly use classification and regression algorithms.
The application of deep learning and reinforcement learning is still limited to some hedge funds and mutual funds.
The main reason that Ai has not had a greater proliferation in financial services are due primarily to poor data quality.
Although the financial market has a large amount of structured data, like most industries it also has much more unstructured data.
Investment decisions are more often based on these unstructured data, such as government policies, news, and so on.
The current algorithm is not yet able to understand this type of data very effectively.
Black boxes and the limits of interpretability are also major headwinds for the spread of artificial intelligence algorithms, which are generally a black box.
After the learning is completed, people do not know the specific principle of the prediction, and the weights in the model also cannot be explained. However, in financial services, in order to control risks, both regulators and investors have higher requirements for the interpretability of the model.
Therefore, logistic regression and classification algorithms are much more popular than deep learning since they are explicable. In addition, some studies have shown that by adding some constraints in learning, although it will reduce the excess return of the model, the model can better meet the regulatory requirements.
In the future, if a large number of investment decisions are made by machines, once the investment fails, who should bear this responsibility? Obviously, computers can’t take responsibility for investment failures.
Therefore, hedge funds and fintech companies are one area of the economy that can withstand greater risks in applying machine learning algorithms to the business practices. Their employees will generally use caution to safeguard against their own career failure as an analyst or modeler.
The application of artificial intelligence in the financial industry is still in a very preliminary state, and there are many challenges and difficulties that need to be overcome.
But the best way to evaluate a tool or a method is the profit it can contribute to the company. Clearly, artificial intelligence has brought efficiency and profit improvements in many areas of financial services. We can believe that in the future, based on the algorithms upgrade, artificial intelligence can add more value to financial services industry.
Artificial Intelligence In Financial Services