Leveraging prompt engineering to enhance financial market integrity and risk management
This study examines prompt engineering's effect on LLM performance, enhancing financial risk analysis and market integrity through comparative experiments.
What it examines
This study investigates prompt engineering to enhance financial risk analysis with LLMs. It compares ChatGPT-4 and Google Gemini in generating accurate financial insights for credit and market risk evaluations. Methods involve experimental testing of prompt configurations to reduce errors and ensure alignment with regulatory frameworks.
What it concludes
The study concludes prompt engineering significantly improves LLM performance in financial risk management. ChatGPT-4 excelled over Google Gemini with over 30% better accuracy and version upgrades yielded a further 20% improvement. This research supports safer market practices and regulatory compliance, with applications in credit and market risk analysis and modeling.
Evidence objects
The study reveals that optimizing prompts improves large language model performance in financial market integrity, enhancing risk management practices by lowering error rates by around 20% during complex inquiries, notably.
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Comparative experiments show ChatGPT-4 delivers over 30% more accurate financial insights than Google Gemini, while its version 4 achieves a 20% enhancement in regulatory alignment compared to its previous iteration.
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Researchers systematically compared varied AI prompt configurations to enhance credit risk, market evaluation, and financial modeling; however, notable uncertainties persist regarding the long-term adaptability and scalability of these innovative techniques.
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This paper innovatively applies prompt engineering to enhance $LLM$ outputs for quantitative risk management, offering compelling empirical comparisons such as $ChatGPT-4$ versus $Google~Gemini$, version improvements, and error reduction metrics. Its novel application is exciting for practitioners though methodologically incremental, balancing fresh insights with modest originality to stimulate further research interest.
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Raw abstract and provenance
This paper presents a comprehensive investigation into the role of prompt engineering in optimizing the effectiveness of large language models (LLMs) like ChatGPT-4 and Google Gemini for financial market integrity and risk management. As AI tools are increasingly integrated into financial services, including credit risk analysis, market risk evaluation, and financial modeling, prompt engineering has become crucial for improving the relevance, accuracy, and contextual alignment of AI-generated outputs. This study evaluates the impact of various prompt configurations in enhancing financial decision-making. Through a series of experiments, the paper compares the performance of ChatGPT-4 and Google Gemini (versions 1.5 and 2.0) in generating actionable insights for credit and market risk analysis. The results reveal that ChatGPT-4 outperforms Google Gemini by over 30% in generating accurate financial insights. Additionally, ChatGPT-4 Version 4 is found to be 20% more effective than Version 3 in risk analysis tasks, particularly in aligning with regulatory frameworks and financial data. These improvements highlight the significant role of prompt engineering in enhancing the precision of financial models. Furthermore, the study explores the reduction of error rates through optimized prompt strategies. In particular, prompt engineering reduces error rates by approximately 20% when assessing complex financial queries.
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