In real life, there are several situations that require strategy control, that is, reinforcement learning, as a method to study decision making and user behavior. As the Shanghai University of Accounting and Finance study indicates, the classical approach to creating AI requires programmers to hand-code each rule that defines software behavior.
We use sentiment analysis as a crucial tool for financial market predictions and investment decisions.
Óscar Barba
Co-founder & CTO of Coinscrap Finance
Improving algorithms is a window of opportunity for banks
Unlike rule-based AI, machine learning programs develop their behavior by examining large amounts of data and finding meaningful correlations. While machine learning and its more advanced subset of deep learning can solve many problems previously thought infeasible for computers, they are based on vast amounts of previously collected data.
This limits its application to areas where labeled data is sparse. This is where reinforcement learning comes into play, according to Yixuan Guo (2022). Human beings and superior animals can engage in continuous interaction with their external environment in order to understand it. Furthermore, we both have the ability to continually learn in order to make more rational decisions.
Machine learning has a number of advantages over human learning, and machine-based knowledge has been shown to far exceed the capabilities of the human brain in terms of knowledge memorization, comprehension, and understanding. The value of using machine learning in finance is becoming increasingly apparent.
The many uses and benefits of sentiment analysis in finance
As banks and other financial institutions strive to improve security or financial analysis and streamline processes, machine learning is becoming the industry’s most widely used technology. It is used to offer new financial forecasting, customer service and data security services. And also, of course, to analyze the sentiment.
In the pre-processing phase, for example, features related to the sentiment of financial news are extracted. This approach is used to predict market trends and make investment decisions based on data. By adding knowledge graphs to the analysis, one can better understand the characteristics of the stock market.
These tools listen to themes, trends and patterns in the media and social networks to extract sentiment that can generate accurate predictions at the macro or micro level. But they are also essential to understand the tastes and preferences of consumers and create a more connected experience with them within digital platforms.
Here is an article on how banks are already applying artificial intelligence.
A personal prediction about the future of sentiment analysis
Financial institutions can boost customer loyalty and satisfaction by leveraging insights gained through this technology. Coupled with data enrichment, you can gain a deep understanding of your spending, analyze recurring transactions, and anticipate future needs. This leads us to the possibility of launching hyper personalized recommendations that improve your relationship with the bank.
So far, the greatest success in sentiment classification has been achieved by using contemporary bidirectional encoder representations of model transducers (BERT). Pre-trained transducer systems were enhanced on a dataset of labeled financial texts to predict news sentiment scores from trusted sources. It is very likely that new studies will emerge in this regard and the algorithms will continue to improve.
At Coinscrap Finance we use sentiment analysis as a crucial tool for financial market predictions and investment decisions. Text classification is basic in many applications, such as web search, opinion mining or event detection.
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About the Autor
Óscar Barba is co-founder and CTO of Coinscrap Finance. He is an expert Scrum Manager with more than 6 years of experience in the collection and semantic analysis of data in the financial sector, classification of bank transactions, deep learning applied to stock market sentiment analysis systems and the measurement of the carbon footprint associated with transactional data.
With extensive experience in the banking and insurance sector, Óscar is finishing his PhD in Information Technology right now. He is an Engineer and Master in Computer Engineering from the University of Vigo and Master in Electronic Commerce from the University of Salamanca. In addition, Scrum Manager and Project Management Certificate from the CNTG, SOA Architecture and Web Services Certificate from the University of Salamanca and more.
