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Revolutionizing Finance with LLMs: An Overview of Applications and Insights
Huaqin Zhao, Zhengliang Liu, Zihao Wu, Yiwei Li, Tianze Yang, Peng Shu, Shaochen Xu, Haixing Dai, Lin Zhao, Hanqi Jiang, Yi Pan, Junhao Chen, Yifan Zhou, Zheyuan Zhang, Zeyu Zhang, Ruitong Sun, Gengchen Mai, Ninghao Liu, Tianming Liu
TL;DR
Financial applications of LLMs face specialized data and high-stakes accuracy requirements, motivating a clearer synthesis of their capabilities and boundaries. This paper surveys LLM use across financial tasks and evaluates GPT-4, finding strong text-processing, sentiment-analysis, and zero-shot abilities while emphasizing that direct computational finance remains supplementary.
Problem
Financial applications involve specialized data and high-risk decisions, creating a need to understand how LLM successes can address finance-specific difficulties.
Method
The paper surveys LLM applications and technical approaches across finance, then assesses GPT-4 across various financial tasks.
Results
LLMs show strong text processing, sentiment analysis, and zero-shot learning abilities across the paper’s review of financial tasks.
Takeaways & Limitations
LLMs can enhance financial models and decision-making processes, particularly by interpreting extensive textual data and investor sentiment.
Takeaways & Limitations
LLMs remain largely supplementary rather than standalone solutions for optimization and quantitative trading because they cannot directly perform computational tasks.
Abstract
from arXiv · showhide
In recent years, Large Language Models (LLMs) like ChatGPT have seen considerable advancements and have been applied in diverse fields. Built on the Transformer architecture, these models are trained on extensive datasets, enabling them to understand and generate human language effectively. In the financial domain, the deployment of LLMs is gaining momentum. These models are being utilized for automating financial report generation, forecasting market trends, analyzing investor sentiment, and offering personalized financial advice. Leveraging their natural language processing capabilities, LLMs can distill key insights from vast financial data, aiding institutions in making informed investment choices and enhancing both operational efficiency and customer satisfaction. In this study, we provide a comprehensive overview of the emerging integration of LLMs into various financial tasks. Additionally, we conducted holistic tests on multiple financial tasks through the combination of natural language instructions. Our findings show that GPT-4 effectively follow prompt instructions across various financial tasks. This survey and evaluation of LLMs in the financial domain aim to deepen the understanding of LLMs' current role in finance for both financial practitioners and LLM researchers, identify new research and application prospects, and highlight how these technologies can be leveraged to solve practical challenges in the finance industry.
1 Introduction
LLMs are increasingly being applied to finance because they can process complex, large-scale financial information, but specialized terminology, regulations, market dynamics, and high-stakes decisions create accuracy and reliability challenges. This review surveys major financial applications and assesses GPT-4 across varied tasks.
- Motivation: LLMs can analyze financial reports, market news, and investor communications to support trend analysis, risk assessment, investment decisions, and financial advice.Their natural-language capabilities also support real-time responses to financial queries.
- Challenges: Financial applications require high accuracy and reliability because domain data, terminology, regulations, market dynamics, and decisions are specialized and high risk.These conditions make dependable model outputs a central challenge.
- Contributions: The review surveys LLM applications across financial engineering, forecasting, risk management, and real-time question answering.It synthesizes existing literature across four independent task categories.
- Contributions: The article summarizes technical approaches, examines investment applications, and provides a foundation for researchers studying LLMs in finance.This contribution complements the literature survey and task evaluation.
- Evaluation: The study assesses GPT-4 across various financial tasks as part of its empirical evaluation.The evaluation is presented alongside the broader review of LLM applications.
- Scope: The review combines a synthesis of major findings with discussion of unresolved issues and future research directions.Its stated aim is to connect current results with subsequent efforts in the field.
2 Related Work
Related work describes the Transformer and token-generation foundations of LLMs, then connects them to language applications, information extraction, question answering, sentiment analysis, and financial forecasting. It also distinguishes established approaches for named entity recognition and financial sentiment analysis.
- Large Language Models: Transformers use encoder and decoder components with self-attention and feed-forward layers to manage long-range dependencies in sequences.Self-attention derives queries, keys, and values from inputs and produces weighted combinations of value vectors.
- Large Language Models: Self-attention computes normalized attention scores between queries and keys, then uses them to weight value vectors.This mechanism allows outputs to combine inputs according to their contextual relevance.
- Large Language Models: Decoder-only models such as GPT generate text unidirectionally, whereas encoder-decoder models separately encode inputs and decode target sequences.These architectural differences support different language-processing tasks.
- Large Language Models: Token generation constructs vocabularies, predicts token probabilities, and uses decoding strategies such as greedy decoding or beam search.Byte-Pair Encoding supports subword-based vocabulary construction.
- Applications: LLMs support report generation, conversational agents, information extraction, summarization, and sentiment analysis across language-intensive domains.These applications rely on their ability to generate, interpret, and condense text.
- Named Entity Recognition: Named entity recognition identifies and classifies entities such as people, organizations, time expressions, and financial terms using rule-based, machine-learning, or deep-learning methods.It supports information extraction, question answering, content analysis, and knowledge-graph construction.
- Financial Sentiment Analysis: Financial sentiment analysis includes lexicon-based and machine-learning approaches, with existing methods differing in sentiment weighting, neutral-sentiment handling, and classification performance.The literature includes dictionary, corpus-based, unsupervised, and supervised techniques.
- Question Answering: LLMs answer specialized questions and can improve arithmetic, deductive reasoning, and commonsense tasks when complex problems are decomposed through structured sequential prompts.Their broad text-derived knowledge supports question answering across domains including finance.
3.1 Financial Engineering
LLMs extend financial engineering by extracting nuanced information from unstructured financial text and incorporating it into quantitative trading and portfolio optimization. Their integration supports more adaptive and flexible investment analysis, while robo-advisory personalization remains limited.
- Financial Engineering: Financial engineering applications of LLMs focus on quantitative trading and portfolio optimization.These tasks combine finance, mathematics, and computer science with language-based analysis.
- Quantitative Trading: LLMs extract implicit sentiment, context, sarcasm, and financial jargon from analysts’ reports, market news, and financial statements for quantitative trading.This addresses the difficulty traditional models face with unstructured data and subtle market information.
- Quantitative Trading: Integrating LLM-derived sentiment with quantitative models provides a more holistic approach to investment decisions and can improve strategy robustness in rapidly changing markets.The approach combines numerical precision with nuanced interpretation of textual market information.
- Portfolio Optimization: LLMs supplement portfolio optimization by analyzing market reports, news, and financial statements to uncover sentiments, trends, risks, and opportunities.This complements historical-data-based methods that may not capture geopolitical events or sudden market shifts.
- Robo-advisors: Robo-advisors use LLMs to process extensive data, tailor portfolios to user risk preferences, and update allocations as market conditions change.The systems continuously monitor portfolio performance and balance expected returns against user-defined risk thresholds.
- Robo-advisors: Studies of robo-advisory services find limited personalization, with recommendations often favoring broadly applicable principles over complex customized strategies.The German market study examined approximately 78 assets and 243,000 portfolio pairs; explainability, privacy, and security were cited considerations.
3.2 Financial Forecasting
LLMs support financial forecasting by mining textual signals across M&A, insolvency, and market-trend tasks. GPT-4-based analysis is presented as adaptive and interpretable, although the supplied passages do not provide a quantitative forecasting result.
- M&A Forecasting: LLMs analyze reports, news, press releases, historical cases, and social media to identify signals that may precede M&A activity.These sources support trend detection, sentiment analysis, pattern discovery, and monitoring of speculative information.
- Insolvency Forecasting: LLMs support insolvency forecasting by detecting financial distress signals in disclosures, news, corporate statements, regulatory filings, and communication tone.Textual analysis complements numerical bankruptcy models and can reveal linguistic or disclosure patterns associated with financial difficulties.
- Market Trend Forecasting: GPT-4 market-trend analysis addresses the difficulty of adapting traditional forecasting methods to stochastic markets with interdependent economic, geopolitical, and sentiment factors.Traditional quantitative models may struggle with market-sentiment subtleties and rapid shifts in global economic conditions.
- Market Trend Forecasting: NLP complements quantitative forecasting by processing news, financial reports, and social media to identify market sentiment, trends, patterns, and correlations.The textual signals may reveal information not immediately apparent from numerical data alone.
- Market Trend Forecasting: GPT-4 is described as adaptable to new information, customizable across analysis targets, and capable of producing interpretable rationales for forecasts.The supplied passages characterize the experiment’s outcomes as accurate and interpretable but do not report a numeric metric.
3.3 Financial Risk Management
LLMs are presented as tools for financial risk management across credit assessment, ESG scoring, fraud detection, and compliance. Their value lies in processing large-scale unstructured information and adapting to changing standards, while GPT-4 ESG scoring remains an area with limited prior exploration.
- Credit and Risk Assessment: Credit and risk assessment supports lending, investment-risk evaluation, company-health analysis, and decisions about loan policies and interest rates.Traditional approaches have predominantly relied on rules or machine-learning algorithms.
- ESG Scoring: ESG scoring evaluates companies’ environmental, social, and governance practices using public, proprietary, and other tangible or intangible data.These scores help investors and portfolio managers assess companies and construct sustainability-oriented portfolios.
- ESG Scoring: GPT-4 is proposed for ESG scoring because it can rapidly process unstructured sources such as sustainability reports, news articles, and social media posts.The paper presents this integration as a way to provide deeper, more accurate, and up-to-date ESG insights.
- Fraud Detection: Fraud detection is evaluated using the PaySim simulated mobile-money-transactions dataset to assess GPT-4’s effectiveness.The application responds to increasing risks from sophisticated financial crimes and large transaction volumes.
- Financial Compliance: Zero-shot LLMs can adapt to changing compliance standards without repeated fine-tuning, supporting audits, transaction monitoring, reporting, and disclosure.This is useful when regulation checklists change rapidly and models trained on outdated standards become obsolete.
3.4 Financial Real-Time Question Answering
GPT-4 is presented as a financial-education tool that explains complex concepts, adapts instruction to learners, and supports interactive practice. Its usefulness is bounded by knowledge timeliness, accuracy, and compliance requirements.
- Financial education: GPT-4 can simplify complex financial concepts into more understandable language for learners.The paper highlights securities markets, portfolio diversification, and risk management as examples of complex topics.
- Financial education: GPT-4 can personalize financial education by adjusting content and difficulty to learners’ progress, interests, and needs.The paper contrasts basic instruction for beginners with advanced analysis for experienced learners.
- Financial education: Interactive Q&A, simulated scenarios, and real-time feedback can create a more engaging learning environment and improve practical skills and problem-solving abilities.
- Limitations: GPT-4’s financial-education use is limited by reliance on existing knowledge, which may leave recent market and regulatory developments underrepresented.
- Limitations: Financial-education deployments must ensure that generated information and advice comply with relevant laws and are ethically responsible.
- Conclusion: The paper concludes that GPT-4 could become an auxiliary tool for helping users understand and apply financial knowledge.
4 GPT-4 Empowered Financial Tasks Evaluations
The evaluation uses diverse financial datasets and practical tasks to test GPT-4 with zero-shot, Chain-of-Thought, and one-shot prompts. The tasks cover sentiment, entities, question answering, stock movements, summarization, and fraud detection.
- Evaluation design: The evaluation combines practical financial tasks, benchmark datasets, instruction prompts, and evaluation indicators.
- Datasets: Six datasets cover news, analytical reports, tweets, time series, tabular data, and textual content designed to represent real-world finance scenarios.
- Financial sentiment: Financial sentiment analysis uses the Financial Phrase Bank and FiQA-SA datasets to interpret sentiment in financial narratives.
- Information extraction and question answering: The evaluation also includes financial named-entity recognition and question answering over SEC agreements and S&P 500 earnings reports, including multi-turn dialogues.
- Stock prediction: Stock-movement prediction is framed as binary classification using historical prices and relevant tweets, with BigData22 as the dataset.
- Prompting strategies: The study compares vanilla zero-shot, Chain-of-Thought zero-shot, and one-shot prompting, while adding prompt components did not substantially improve performance.
5 Experimental Results
Across the tested financial tasks, the paper reports precise execution, strong zero-shot learning and mathematical reasoning, and particularly strong language sentiment analysis. Fraud detection is illustrated with five correct classifications on PaySim transactions.
- Overall results: The authors report precise execution across the tested financial tasks, including zero-shot learning, mathematical reasoning, and language sentiment analysis.
- Evaluation: The evaluation assesses GPT-4 by comparing recommendations with real-world financial data and historical market performance across financial scenarios and datasets.
- Fraud detection: 5 out of 5 PaySim transactions are classified correctly in the fraud-detection illustration.Green indicates normal transactions, while yellow indicates suspicious or fraudulent transactions.
6 Limitation and Future work
The paper characterizes LLMs as useful for text and sentiment analysis but limited in optimization and quantitative trading. It proposes hybrid systems that combine LLM text processing with advanced quantitative models.
- Limitations: LLMs cannot directly perform computational tasks in optimization and quantitative trading, so they currently augment rather than replace quantitative models.
- Future work: Future work could integrate LLM text processing with sophisticated quantitative trading algorithms in hybrid systems.
- Future work: The proposed research directions also include improving interpretability and reliability and applying LLMs to market-trend predictive analytics.
7 Conclusion
The review examines GPT-4 across 11 financial tasks, finding particular strength in text processing, sentiment analysis, and zero-shot learning. It also identifies direct computational tasks such as optimization and quantitative trading as important limitations.
- GPT-4 was evaluated across 11 financial tasks to characterize LLM capabilities and constraints in finance.
- LLMs show strong capabilities in text processing, sentiment analysis, and zero-shot learning, supporting interpretation of market dynamics and investor sentiment.
- Direct computational tasks, particularly optimization and quantitative trading, remain areas where LLMs play largely supplementary roles.