Paper: arXiv 2305.07466

Authors: Nadeem Malibari, Iyad Katib, Rashid Mehmood

Abstract

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.

Complexity vs Empirical Score

  • Math Complexity: 2.0/10
  • Empirical Rigor: 3.0/10
  • Quadrant: Philosophers — conceptual discussion, limited math and data

Why this score: This paper is a qualitative systematic review following the PRISMA framework rather than a novel quantitative study. While it offers a structured summary of Reinforcement Learning applications in Fintech, it lacks deep mathematical derivations and empirical backtesting or statistical validation.

Research Flowchart

  flowchart TD
    A["Research Goal:<br>Evaluate RL Applications & Performance in Fintech"] --> B["Methodology:<br>PRISMA Systematic Review Methodology"]
    B --> C["Data / Inputs:<br>Literature Publications (2018 Onward)"]
    C --> D["Analysis Process:<br>Assess Prediction Accuracy, Risk, Scalability & Profitability"]
    D --> E["Core Applications:<br>Robo-Advising, Portfolio Optimization & Execution"]
    E --> F["Key Outcome:<br>RL Strategies Outperform State-of-the-Art Algorithms"]