Towards Automated Regulatory Compliance Verification in Financial Auditing with Large Language Models

Berger, Armin; Hillebrand, Lars; Leonhard, David; Deußer, Tobias; De Oliveira, Thiago Bell Felix; Dilmaghani, Tim; Khaled, Mohamed; Kliem, Bernd; Loitz, Rudiger; Bauckhage, Christian; Sifa, Rafet

doi:10.1109/bigdata59044.2023.10386518

Cited by 1 publication

(2 citation statements)

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“…This transformation is particularly evident in the domain of regulatory compliance, where traditional models are being reevaluated in light of AI-driven approaches. Berger et al (2023) delve into this shift, focusing on the auditing of financial documents. Historically labor-intensive, this process is being revolutionized by AI-driven solutions that streamline the alignment of financial reports with legal accounting standards.…”

Section: Comparative Analysis Of Traditional Vs Ai-driven Compliance ...mentioning

confidence: 99%

“…Their research emphasizes the efficiency of Large Language Models (LLMs) in regulatory compliance, comparing open-source models like Llama-2 with proprietary ones such as OpenAI's GPT models. The study finds that while open-source models excel in detecting non-compliance, proprietary models offer broader applicability, especially in non-English contexts (Berger et al, 2023). Oriji et al (2023) provide a comprehensive review of the evolution of financial technology in Africa, highlighting the implications and future prospects of AI-driven financial services.…”

Section: Comparative Analysis Of Traditional Vs Ai-driven Compliance ...mentioning

confidence: 99%

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Automating Financial Regulatory Compliance With Ai: A Review and Application Scenarios

Oluwatobi Opeyemi Adeyelu,

Chinonye Esther Ugochukwu,

Mutiu Alade Shonibare

2024

Financ. account. res. j.

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This scholarly paper delves into the transformative realm of Artificial Intelligence (AI) in financial regulatory compliance, offering a classical and engaging exploration of its multifaceted impact. Against an increasingly complex financial landscape backdrop, the study aims to unravel the intricacies of AI integration in compliance models, juxtaposing traditional methodologies with cutting-edge AI-driven approaches. The scope of the paper encompasses a systematic literature review and qualitative analysis, focusing on the evolution of AI in financial services, its necessity for enhanced compliance efficiency, and a comparative analysis of traditional versus AI-driven compliance models. The study synthesizes findings from diverse peer-reviewed articles, case studies, and comparative analyses by employing a meticulous methodology. It illuminates the state-of-the-art AI technologies in financial compliance, evaluates their effectiveness in various regulatory contexts, and identifies key performance indicators for AI compliance. The paper also critically examines the challenges and limitations observed in AI compliance solutions alongside emerging trends and future directions. The main conclusions reveal that AI significantly enhances compliance efficiency and accuracy, adeptly addresses complex regulatory challenges, and has strategic implications for financial institutions. However, the study also highlights the need for balancing innovation with regulatory and ethical compliance. Recommendations include the adoption of proactive regulatory frameworks, stakeholder engagement, and the development of robust AI governance models. This paper contributes to the academic discourse on AI in financial services, guiding policymakers, regulators, and industry practitioners. It advocates for a harmonized approach to AI integration, ensuring responsible and effective utilization in the financial sector. Keywords: Artificial Intelligence, Financial Regulatory Compliance, Systematic Literature Review, AI Technologies, Regulatory Challenges, Strategic Implications.

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Section: Comparative Analysis Of Traditional Vs Ai-driven Compliance ...mentioning

confidence: 99%

Section: Comparative Analysis Of Traditional Vs Ai-driven Compliance ...mentioning

confidence: 99%

Automating Financial Regulatory Compliance With Ai: A Review and Application Scenarios

Oluwatobi Opeyemi Adeyelu,

Chinonye Esther Ugochukwu,

Mutiu Alade Shonibare

2024

Financ. account. res. j.

View full text Add to dashboard Cite

show abstract

Towards Automated Regulatory Compliance Verification in Financial Auditing with Large Language Models

Cited by 1 publication

References 19 publications

Automating Financial Regulatory Compliance With Ai: A Review and Application Scenarios

Automating Financial Regulatory Compliance With Ai: A Review and Application Scenarios

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