The List / Categories
Use surveys to map the field before selecting a model or dataset. The list brings together broad finance-LLM surveys and narrower reviews; their coverage windows and definitions can differ substantially.
Read a survey first, then move to the benchmarks guide to identify how the claims were tested.
This survey synthesizes the state-of-the-art in applying large language models to finance, reviewing solution architectures from zero-shot to custom pretraining, proposing a cost-aware decision framework for adoption, and highlighting limitations regarding hallucination and bias.
This survey reviews the integration of Large Language Models into finance, categorizing applications into engineering, forecasting, risk management, and QA, while empirically evaluating GPT-4's ability to follow instructions across these diverse financial tasks.
This survey provides a comprehensive overview of Financial Large Language Models (FinLLMs), comparing their evolution from general-domain models, analyzing training techniques, evaluating performance across six benchmark tasks, and identifying challenges and future directions for the field.
This survey provides a comprehensive review of large language models in finance, categorizing applications into linguistic tasks, sentiment analysis, time series, reasoning, and agent-based modeling, while detailing specific models, datasets, benchmarks, and critical challenges like lookahead bias and data pollution.
This survey provides a comprehensive review of large language models in finance, healthcare, and law, categorizing tasks, models, and ethical challenges to guide future interdisciplinary research in high-stakes domains.
This scoping review synthesizes 264 recent papers on ChatGPT and LLMs in accounting and finance, identifying three core themes: domain applications, LLMs as research tools, and professional implications, while providing a framework for future inquiry.
This survey systematically reviews 27 studies on LLM-powered trading agents, categorizing them into 'LLM as Trader' and 'LLM as Alpha Miner' architectures, analyzing their data inputs, evaluation metrics, and identifying key limitations such as short backtesting periods and lack of cost considerations.
This 2024 review systematically analyzes 90 papers on generative AI in finance using BERTopic, identifying three main themes: LLM applications for financial tasks, risks and regulatory challenges, and synthetic data generation via GANs.
This 2025 review synthesizes the landscape of AI agent frameworks like LangGraph, CrewAI, and AutoGen, evaluating their architectures, financial applications in risk and trading, and critical challenges regarding regulatory compliance, explainability, and risk alignment.
This survey systematically reviews the evolution of quantitative investment from traditional statistical models to deep learning and large language models, focusing on how AI enhances the alpha strategy pipeline across data processing, prediction, and execution.
This survey bridges financial practice and LLM research by proposing a dual-perspective framework that maps five core financial domains to specific agent tasks, catalogs over 30 benchmarks and 20 models, and identifies critical gaps in numerical reasoning, real-time adaptability, and regulatory compliance.
This paper demonstrates that multi-agent LLM frameworks can effectively automate the cross-verification of underlying assets in structured finance, achieving near-perfect accuracy in matching loan applications to bank statements while offering a cost-effective alternative to manual auditing.
This 2025 survey synthesizes the landscape of generative AI agent frameworks and their applications in finance, highlighting productivity gains, multi-agent architectures, and deployment challenges.
This survey categorizes LLM integration in finance into four frameworks, highlighting that hybrid and agent-based methods significantly outperform baselines in stock prediction and portfolio optimization, though challenges in hallucination and real-time adaptability remain.
This survey bridges the gap between rapid LLM advancements and cautious financial adoption by comprehensively reviewing datasets, models, and applications across trading, risk, and ESG, while outlining future research directions for reasoning, RAG, and multi-agent systems.
This structured literature review analyzes 14 recent studies to map the current state, application scenarios, and significant challenges of integrating Large Language Models and Generative AI into core banking operations, highlighting a gap between experimental potential and productive deployment due to regulatory and reliability constraints.
This paper presents the first systematic review and taxonomy of LLM-based credit risk assessment, categorizing 60 studies by model architecture, data modality, interpretability mechanisms, and application domains to highlight trends and research gaps.
This 2025 scientific review synthesizes the application of autonomous AI agents across finance, contrasting traditional rule-based and reinforcement learning systems with emerging LLM-driven architectures to evaluate their efficacy, ethical implications, and future research directions.
This survey synthesizes over fifty primary studies to propose a task-centered taxonomy for large language models in equity return prediction and trading, emphasizing the transition from feature-centric text mining to end-to-end agentic decision systems with rigorous evaluation standards.
This review synthesizes LLM applications in stock forecasting from a hedge-fund perspective, critiquing academic practices for ignoring market frictions like data leakage, illiquidity, and regime shifts while proposing rigorous evaluation standards for production deployment.