Review of Gen AI Models for Financial Risk Management: Architectural Frameworks and Implementation Strategies

Satyadhar  Joshi

doi:10.69968/ijisem.2025v4i2207-222

Authors

Satyadhar Joshi Independent Researcher, Alumna, International MBA, Bar-Ilan University, Israel.

DOI:

https://doi.org/10.69968/ijisem.2025v4i2207-222

Keywords:

Generative AI, financial analytics, risk management, credit scoring, large language models, feature engineering

Abstract

The rapid advancement of generative artificial intelligence (Gen AI) has revolutionized various domains, including financial analytics. This paper provides a comprehensive review of the applications, challenges, and future directions of Gen Al in financial analytics. We explore its role in risk management, credit scoring, feature engineering, and macroeconomic simulations, while addressing limitations such as data quality, interpretability, and ethical concerns. By synthesizing insights from recent literature, we highlight the transformative potential of Gen AI and propose frameworks for its effective integration into financial workflows.

This paper presents a systematic examination of generative artificial intelligence (Gen AI) applications in financial risk management, focusing on architectural frameworks and implementation methodologies. We analyze the integration of large language models (LLMs) with traditional quantitative finance pipelines, addressing key challenges in feature engineering, risk modeling, and regulatory compliance. The study demonstrates how transformer-based architectures enhance financial analytics through automated data processing, risk factor extraction, and scenario generation. Technical implementations leverage hybrid cloud platforms and specialized Python libraries for model deployment, achieving measurable improvements in accuracy and efficiency. Our findings reveal critical considerations for production systems, including computational optimization, model interpretability, and governance protocols. The proposed architecture combines LLM capabilities with domain-specific modules for credit scoring, value-at-risk calculation, and macroeconomic simulation. Empirical results highlight trade-offs between model complexity and operational constraints, providing actionable insights for financial institutions adopting Gen Al solutions. The paper concludes with recommendations for future research directions in financial Al systems.

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