Credit and risk assessments are cornerstones of the financial landscape,
impacting both individual futures and broader societal constructs. Existing
credit scoring models often exhibit limitations stemming from knowledge myopia
and task isolation. In response, we formulate three hypotheses and undertake an
extensive case study to investigate LLMs' viability in credit assessment. Our
empirical investigations unveil LLMs' ability to overcome the limitations
inherent in conventional models. We introduce a novel benchmark curated for
credit assessment purposes, fine-tune a specialized Credit and Risk Assessment
Large Language Model (CALM), and rigorously examine the biases that LLMs may
harbor. Our findings underscore LLMs' potential in revolutionizing credit
assessment, showcasing their adaptability across diverse financial evaluations,
and emphasizing the critical importance of impartial decision-making in the
financial sector. Our datasets, models, and benchmarks are open-sourced for
other researchers.

研究发现，LLMs 在信用评估中具有潜力，可以克服传统模型的局限，强调金融行业中客观决策的重要性。

强大多数，偏倚少数：通过大型语言模型实现综合信用评分

Empowering Many, Biasing a Few: Generalist Credit Scoring through Large  Language Models

With the investment landscape becoming more competitive, efficiently scaling
deal sourcing and improving deal insights have become a dominant strategy for
funds. While funds are already spending significant efforts on these two tasks,
they cannot be scaled with traditional approaches; hence, there is a surge in
automating them. Many third party software providers have emerged recently to
address this need with productivity solutions, but they fail due to a lack of
personalization for the fund, privacy constraints, and natural limits of
software use cases. Therefore, most major funds and many smaller funds have
started developing their in-house AI platforms: a game changer for the
industry. These platforms grow smarter by direct interactions with the fund and
can be used to provide personalized use cases. Recent developments in large
language models, e.g. ChatGPT, have provided an opportunity for other funds to
also develop their own AI platforms. While not having an AI platform now is not
a competitive disadvantage, it will be in two years. Funds require a practical
plan and corresponding risk assessments for such AI platforms.

投资领域的发展变得愈发竞争，对于基金来说，高效扩展交易资源和提升交易洞察力已成为一种主导策略。这些任务无法通过传统方式进行扩展，因此自动化的需求也随之上升。许多第三方软件服务提供商应运而生，但他们由于缺乏对基金的个性化支持、隐私限制以及软件应用场景的局限性而未能成功。因此，大多数主要基金和许多较小的基金已经开始开发自己的人工智能平台，这对行业来说是一场变革。这些平台通过与基金直接互动变得越来越智能化，并可用于提供个性化的应用案例。最近大型语言模型的发展，例如 ChatGPT，为其他基金开发自己的人工智能平台提供了机会。虽然现在没有 AI 平台不会造成竞争劣势，但两年后将会有变。基金需要一个实际可行的计划以及对这种 AI 平台的风险评估。

投资的人工智能：一个平台颠覆

AI for Investment: A Platform Disruption

Early warning systems (EWS) are prediction algorithms that have recently
taken a central role in efforts to improve graduation rates in public schools
across the US. These systems assist in targeting interventions at individual
students by predicting which students are at risk of dropping out. Despite
significant investments and adoption, there remain significant gaps in our
understanding of the efficacy of EWS. In this work, we draw on nearly a
decade's worth of data from a system used throughout Wisconsin to provide the
first large-scale evaluation of the long-term impact of EWS on graduation
outcomes.
We present evidence that risk assessments made by the prediction system are
highly accurate, including for students from marginalized backgrounds. Despite
the system's accuracy and widespread use, we find no evidence that it has led
to improved graduation rates. We surface a robust statistical pattern that can
explain why these seemingly contradictory insights hold. Namely, environmental
features, measured at the level of schools, contain significant signal about
dropout risk. Within each school, however, academic outcomes are essentially
independent of individual student performance. This empirical observation
indicates that assigning all students within the same school the same
probability of graduation is a nearly optimal prediction.
Our work provides an empirical backbone for the robust, qualitative
understanding among education researchers and policy-makers that dropout is
structurally determined. The primary barrier to improving outcomes lies not in
identifying students at risk of dropping out within specific schools, but
rather in overcoming structural differences across different school districts.
Our findings indicate that we should carefully evaluate the decision to fund
early warning systems without also devoting resources to interventions tackling
structural barriers.

使用威斯康辛州近十年的数据进行大规模评估后发现，尽管风险评估系统的准确性很高，但没有证据表明其提高了毕业率。结构约束是改善教育成果的主要障碍。

威斯康辛公立学校的社会预测困难教训

Difficult Lessons on Social Prediction from Wisconsin Public Schools

Objectives: Discussions of fairness in criminal justice risk assessments
typically lack conceptual precision. Rhetoric too often substitutes for careful
analysis. In this paper, we seek to clarify the tradeoffs between different
kinds of fairness and between fairness and accuracy.
Methods: We draw on the existing literatures in criminology, computer science
and statistics to provide an integrated examination of fairness and accuracy in
criminal justice risk assessments. We also provide an empirical illustration
using data from arraignments.
Results: We show that there are at least six kinds of fairness, some of which
are incompatible with one another and with accuracy.
Conclusions: Except in trivial cases, it is impossible to maximize accuracy
and fairness at the same time, and impossible simultaneously to satisfy all
kinds of fairness. In practice, a major complication is different base rates
across different legally protected groups. There is a need to consider
challenging tradeoffs.

本文结合犯罪学、计算机科学和统计学等多个领域的文献，探讨了刑事司法风险评估中的公平性和准确性之间的权衡，揭示了至少六种不同的公平性，其中一些不仅彼此不兼容，而且与准确性不兼容。研究发现，实际上很难在同一时间达到准确性和公平性的最大化，并且很难同时满足所有公平的要求，需进行权衡和考量。