Review of Gen AI Models for Financial Risk Management
This paper reviews generative AI methods and fine-tuned LLMs with proprietary data to enhance financial risk management and decision-making.
What it examines
This paper reviews the use of Generative AI and Large Language Models (e.g., GPT) in financial risk management. It compares public tool integration versus proprietary model fine-tuning with domain-specific data, aiming to enhance risk analysis, decision-making, and regulatory compliance.
What it concludes
Generative AI can transform financial risk management by enhancing decision-making, compliance, and analytics. Applications include credit risk assessment, market forecasting, and anomaly detection. The study recommends fine-tuning with domain-specific data and human oversight, suggesting further research on scalability, ethics, and synthetic data integration.
Evidence objects
A study integrates advanced generative AI in risk management by fine-tuning language models to improve credit assessments, market forecasts, and anomaly detection, yielding 70% accurate questions and 60% correct classifications.
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This research introduces a prototype pipeline using lightweight tools such as Flask and cURL, and deploys domain-specific fine-tuning with human oversight and explainable AI, addressing biases while ensuring regulatory compliance.
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Real-world financial data analysis using regression confirms data-driven strategies that yield efficiency gains and improved forecasts, while proposalsloan description risk indicators and zero-shot classificationunderscore challenges in scalability, integration, and ethics.
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The paper provides an innovative review and prototype demonstration that integrates GPT-based generative AI into financial risk management. It proposes fine-tuning on proprietary data, anomaly detection, and real-time integration of public tools. Although building upon established methods, its application perspective injects originality and novelty, making it undeniably compelling to read.
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Raw abstract and provenance
In this paper, we propose and demonstrate a prototype for leveraging Generative AI (GenAI) in financial risk analysis, specifically focusing on fine-tuning GPT models with proprietary data. Financial risk modeling, development, validation, and approval require not only advanced AI techniques but also careful implementation, given the vast and complex datasets involved in such tasks. The research underscores the critical importance of human oversight in mitigating potential failures that can arise from fully automated mathematical models. The study explores the application of Large Language Models (LLMs) in various financial risk domains, such as credit risk assessment, market risk forecasting, and anomaly detection. While synthetic data generation is excluded from this work, the research highlights the use of zero-shot classification leveraging Hugging Face models and OpenAI tools. ChatGPT achieved over 70% accuracy in generating relevant questions and demonstrated 60% correctness in classification tasks. Additionally, we present a prototype pipeline that integrates GenAI capabilities into financial workflows, which is implementable on small-scale computing systems. This includes backend testing via Flask and rapid prototyping using cURL commands, offering a practical approach to testing and deploying models. By fine-tuning GenAI with domain-specific data and optimizing decision-making processes, this research highlights the transformative potential of integrating generative AI into financial risk management. The study provides insights into enhancing model efficiency, regulatory compliance, and scalability. Moreover, it addresses critical challenges such as handling large datasets and ensuring ethical AI use in decision-making systems. This work contributes to advancing the adoption of GenAI in financial analytics, paving the way for innovative, robust, and efficient methodologies to support the evolving demands of the financial sector.
Source row: 1697 · abstract type: unknown