This systematic survey explores the correlation between reinforcement learning (RL) and Fintech, focusing on prediction accuracy, complexity, scalability, risks, profitability, and performance. The study collects relevant literature through an exclusive search procedure using keywords such as RL, Deep Q learning, recurrent unit, recurrent Q network, AI, machine learning, and Fintech. After removing duplicates and filtering for relevance to the research topic, 65 full records are selected for evaluation. The evaluation process includes studying and evaluating these publications based on their publication year. Most of the work was done in 2018 with fewer studies in recent years but still significant enough to be included in the research. The selected studies cover various topics related to RL in Fintech including improving generalization in trading using generative adversarial market models and adaptive quantitative trading using imitative deep RL approaches. The survey concludes that RL-based strategies perform considerably better than other state-of-the-art algorithms in Fintech fields. It highlights the potential benefits of RL algorithms in decision-making challenges such as robo-advising, smart order channelling, market making, hedging and options pricing portfolio optimization and optimal execution for organizations dealing with finance. Overall this survey provides valuable insights into the applications of RL in Fintech and emphasizes its significance in achieving improved performance and profitability in financial institutions.
The summary is about how a type of learning called reinforcement learning is connected to the field of financial technology (Fintech). They studied different things like how accurate predictions are, how complicated it is, and if it can be used on a large scale. They looked at 65 different research papers that talked about these topics. Most of the research was done in 2018, but there were still some important studies from more recent years. The studies found that using reinforcement learning in Fintech works better than other methods. It can help with making decisions in finance organizations and improve their performance and profitability."
Definitions
- Correlation: when two things are connected or related to each other
- Reinforcement learning: a type of learning where you get rewarded for doing something right and learn from your mistakes
- Fintech: short for financial technology, which means using technology to do financial tasks like banking or investing
- Prediction accuracy: how often something you predict turns out to be true
- Complexity: how difficult or complicated something is
- Scalability: if something can work well on a large scale or with many people using it
- Risks: possible bad things that could happen
- Profitability: if something makes money or is profitable
- Performance: how well something does its job
- Exclusive search procedure: a special way of looking for information by only focusing on certain keywords
- Keywords: important words used to find specific information
- Duplicates: copies or repeats
Exploring the Correlation Between Reinforcement Learning and Fintech: A Systematic Survey
The financial technology (Fintech) industry is rapidly evolving, with new technologies being developed to improve decision-making processes in various fields. One of these technologies is reinforcement learning (RL), which has been gaining traction due to its potential for improved accuracy, scalability, and profitability. This systematic survey explores the correlation between RL and Fintech, focusing on prediction accuracy, complexity, scalability, risks, profitability, and performance.
Research Methodology
A comprehensive search procedure was conducted using keywords such as RL, Deep Q learning, recurrent unit, recurrent Q network AI machine learning and Fintech. After removing duplicates and filtering for relevance to the research topic 65 full records were selected for evaluation. The evaluation process included studying and evaluating these publications based on their publication year; most of the work was done in 2018 with fewer studies in recent years but still significant enough to be included in the research.
Findings
The selected studies cover various topics related to RL in Fintech including improving generalization in trading using generative adversarial market models and adaptive quantitative trading using imitative deep RL approaches. The survey concludes that RL-based strategies perform considerably better than other state-of-the-art algorithms in Fintech fields. It highlights the potential benefits of RL algorithms in decision-making challenges such as robo-advising smart order channelling market making hedging options pricing portfolio optimization optimal execution for organizations dealing with finance.
Conclusion
Overall this survey provides valuable insights into the applications of RL in Fintech and emphasizes its significance in achieving improved performance and profitability within financial institutions. As more organizations embrace AI solutions it will be interesting to see how they leverage this technology further over time – particularly when it comes to risk management activities where accurate predictions are essential for success
Applications of Reinforcement Learning in the Finance Technology (Fintech) have acquired a lot of admiration lately. Undoubtedly Reinforcement Learning, through its vast competence and proficiency, has aided remarkable results in the field of Fintech. The objective of this systematic survey is to perform an exploratory study on a correlation between reinforcement learning and Fintech to highlight the prediction accuracy, complexity, scalability, risks, profitability and performance. Major uses of reinforcement learning in finance or Fintech include portfolio optimization, credit risk reduction, investment capital management, profit maximization, effective recommendation systems, and better price setting strategies. Several studies have addressed the actual contribution of reinforcement learning to the performance of financial institutions. The latest studies included in this survey are publications from 2018 onward. The survey is conducted using PRISMA technique which focuses on the reporting of reviews and is based on a checklist and four-phase flow diagram. The conducted survey indicates that the performance of RL-based strategies in Fintech fields proves to perform considerably better than other state-of-the-art algorithms. The present work discusses the use of reinforcement learning algorithms in diverse decision-making challenges in Fintech and concludes that the organizations dealing with finance can benefit greatly from Robo-advising, smart order channelling, market making, hedging and options pricing, portfolio optimization, and optimal execution.
