This paper explores the dual impact of digital banks and alternative lenders
on financial inclusion and the regulatory challenges posed by their business
models. It discusses the integration of digital platforms, machine learning
(ML), and Large Language Models (LLMs) in enhancing financial services
accessibility for underserved populations. Through a detailed analysis of
operational frameworks and technological infrastructures, this research
identifies key mechanisms that facilitate broader financial access and mitigate
traditional barriers. Additionally, the paper addresses significant regulatory
concerns involving data privacy, algorithmic bias, financial stability, and
consumer protection. Employing a mixed-methods approach, which combines
quantitative financial data analysis with qualitative insights from industry
experts, this paper elucidates the complexities of leveraging digital
technology to foster financial inclusivity. The findings underscore the
necessity of evolving regulatory frameworks that harmonize innovation with
comprehensive risk management. This paper concludes with policy recommendations
for regulators, financial institutions, and technology providers, aiming to
cultivate a more inclusive and stable financial ecosystem through prudent
digital technology integration.

本研究探讨数字银行和替代贷方对金融包容性的双重影响以及他们的商业模式所带来的监管挑战，通过对运营框架和技术基础设施的详细分析，确定了促进更广泛金融准入并缓解传统障碍的关键机制；此外还解决了与数据隐私、算法偏见、金融稳定和消费者保护有关的重要监管问题，通过定量金融数据分析与行业专家的定性见解相结合的混合方法研究，阐明了利用数字技术促进金融包容性的复杂性；研究结果强调了需要演进的监管框架，以将创新与全面风险管理相协调；本文最后提出了针对监管机构、金融机构和技术提供商的政策建议，旨在通过审慎的数字技术整合培育一个更具包容性和稳定性的金融生态系统。

加强金融包容性及监管挑战：数字银行和替代信贷在数字平台、机器学习和大型语言模型整合中的关键分析

Enhancing Financial Inclusion and Regulatory Challenges: A Critical  Analysis of Digital Banks and Alternative Lenders Through Digital Platforms,  Machine Learning, and Large Language Models Integration

The rise of algorithmic pricing raises concerns of algorithmic collusion. We
conduct experiments with algorithmic pricing agents based on Large Language
Models (LLMs), and specifically GPT-4. We find that (1) LLM-based agents are
adept at pricing tasks, (2) LLM-based pricing agents autonomously collude in
oligopoly settings to the detriment of consumers, and (3) variation in
seemingly innocuous phrases in LLM instructions ("prompts") may increase
collusion. These results extend to auction settings. Our findings underscore
the need for antitrust regulation regarding algorithmic pricing, and uncover
regulatory challenges unique to LLM-based pricing agents.

通过对大型语言模型（LLMs）以及具体的 GPT-4 算法进行定价任务实验，我们发现 LLM 算法定价代理在垄断市场中自主勾结，对消费者造成不利影响，并且 LLM 指令中表面无关短语的变化可能增加勾结行为，这些结果同样适用于拍卖设置，凸显了需要针对算法定价实施反垄断法规的必要性，揭示了基于 LLM 的定价代理面临的独特监管挑战。

大型语言模型的算法勾结

Algorithmic Collusion by Large Language Models

This chapter provides a comprehensive discussion on AI regulation in the
European Union, contrasting it with the more sectoral and self-regulatory
approach in the UK. It argues for a hybrid regulatory strategy that combines
elements from both philosophies, emphasizing the need for agility and safe
harbors to ease compliance. The paper examines the AI Act as a pioneering
legislative effort to address the multifaceted challenges posed by AI,
asserting that, while the Act is a step in the right direction, it has
shortcomings that could hinder the advancement of AI technologies. The paper
also anticipates upcoming regulatory challenges, such as the management of
toxic content, environmental concerns, and hybrid threats. It advocates for
immediate action to create protocols for regulated access to high-performance,
potentially open-source AI systems. Although the AI Act is a significant
legislative milestone, it needs additional refinement and global collaboration
for the effective governance of rapidly evolving AI technologies.

欧盟 AI 监管、混合监管策略、AI 法案等关键词所涵盖的论文讨论了 AI 监管的复杂性以及现行 AI 法案的不足，并呼吁建立协议以规范高性能开源 AI 系统的访问，以进一步完善 AI 技术的有效治理。