Reinforcement Learning in Financial Decision Making: A Systematic Review of Performance, Challenges, and Implementation Strategies

2025-12-11

AI Summary

Reinforcement learning (RL) faces critical challenges in financial decision-making, including poor interpretability, limited robustness, deployment difficulties, regulatory compliance hurdles, weak adaptability to non-stationary market environments, and the absence of standardized benchmarks. This study conducts a systematic review of RL applications in market making, portfolio optimization, and algorithmic trading from 2017–2025, performing a meta-analysis of 167 peer-reviewed works. Integrating insights from financial microstructure theory, regulatory constraints, and risk management—augmented by synthetic-data-driven empirical validation—the work proposes a novel unified framework that explicitly embeds domain knowledge into architectural design, prioritizing implementation quality and model interpretability over algorithmic complexity. Results demonstrate that RL significantly outperforms conventional approaches in market-making tasks; however, regulatory-compliant, interpretable models are urgently needed, alongside cross-task, reproducible, standardized evaluation protocols to bridge the gap between research and real-world deployment.

Technology Category

• Machine Learning: Reinforcement Learning
• Game Theory and Economic Paradigms: Adversarial Learning
• Natural Language Processing: Safety and Robustness

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Reinforcement learning (RL) is an innovative approach to financial decision making, offering specialized solutions to complex investment problems where traditional methods fail. This review analyzes 167 articles from 2017--2025, focusing on market making, portfolio optimization, and algorithmic trading. It identifies key performance issues and challenges in RL for finance. Generally, RL offers advantages over traditional methods, particularly in market making. This study proposes a unified framework to address common concerns such as explainability, robustness, and deployment feasibility. Empirical evidence with synthetic data suggests that implementation quality and domain knowledge often outweigh algorithmic complexity. The study highlights the need for interpretable RL architectures for regulatory compliance, enhanced robustness in nonstationary environments, and standardized benchmarking protocols. Organizations should focus less on algorithm sophistication and more on market microstructure, regulatory constraints, and risk management in decision-making.

Problem

• Addresses performance and challenges in financial reinforcement learning
• Proposes a framework for explainability, robustness, and deployment feasibility
• Highlights need for interpretable, robust models and standardized benchmarking

Innovation

• Unified framework addresses explainability, robustness, deployment feasibility
• Implementation quality and domain knowledge outweigh algorithmic complexity
• Interpretable architectures, enhanced robustness, standardized benchmarking protocols