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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 inthe field of Fintech. The objective of this systematic survey is to perform anexploratory study on a correlation between reinforcement learning and Fintechto highlight the prediction accuracy, complexity, scalability, risks,profitability and performance. Major uses of reinforcement learning in financeor Fintech include portfolio optimization, credit risk reduction, investmentcapital management, profit maximization, effective recommendation systems, andbetter price setting strategies. Several studies have addressed the actualcontribution of reinforcement learning to the performance of financialinstitutions. The latest studies included in this survey are publications from2018 onward. The survey is conducted using PRISMA technique which focuses onthe reporting of reviews and is based on a checklist and four-phase flowdiagram. The conducted survey indicates that the performance of RL-basedstrategies in Fintech fields proves to perform considerably better than otherstate-of-the-art algorithms. The present work discusses the use ofreinforcement learning algorithms in diverse decision-making challenges inFintech and concludes that the organizations dealing with finance can benefitgreatly from Robo-advising, smart order channelling, market making, hedging andoptions pricing, portfolio optimization, and optimal execution.

Systematic Review on Reinforcement Learning in the Field of Fintech