This paper tackles the problem of mitigating catastrophic risk (which is risk
with very low frequency but very high severity) in the context of a sequential
decision making process. This problem is particularly challenging due to the
scarcity of observations in the far tail of the distribution of cumulative
costs (negative rewards). A policy gradient algorithm is developed, that we
call POTPG. It is based on approximations of the tail risk derived from extreme
value theory. Numerical experiments highlight the out-performance of our method
over common benchmarks, relying on the empirical distribution. An application
to financial risk management, more precisely to the dynamic hedging of a
financial option, is presented.

在顺序决策过程中，解决减轻灾难性风险的问题，通过基于极值理论的尾风险近似导出的一种政策梯度算法 (POTPG)，在金融风险管理中的动态对冲金融期权中得到了良好的实验表现。

基于极值理论的大灾风险感知强化学习

Catastrophic-risk-aware reinforcement learning with  extreme-value-theory-based policy gradients

Financial crimes fast proliferation and sophistication require novel
approaches that provide robust and effective solutions. This paper explores the
potential of quantum algorithms in combating financial crimes. It highlights
the advantages of quantum computing by examining traditional and Machine
Learning (ML) techniques alongside quantum approaches. The study showcases
advanced methodologies such as Quantum Machine Learning (QML) and Quantum
Artificial Intelligence (QAI) as powerful solutions for detecting and
preventing financial crimes, including money laundering, financial crime
detection, cryptocurrency attacks, and market manipulation. These quantum
approaches leverage the inherent computational capabilities of quantum
computers to overcome limitations faced by classical methods. Furthermore, the
paper illustrates how quantum computing can support enhanced financial risk
management analysis. Financial institutions can improve their ability to
identify and mitigate risks, leading to more robust risk management strategies
by exploiting the quantum advantage. This research underscores the
transformative impact of quantum algorithms on financial risk management. By
embracing quantum technologies, organisations can enhance their capabilities to
combat evolving threats and ensure the integrity and stability of financial
systems.

量子算法对金融风险管理具有转变性影响，通过利用量子计算机固有的计算能力，可以提供强大且有效的解决方案，包括金融犯罪检测、预防洗钱、加密货币攻击和市场操纵等方面的问题，并能支持加强的金融风险管理分析。

量子算法：金融犯罪预防的新领域

Quantum Algorithms: A New Frontier in Financial Crime Prevention

The paradigm of vertical federated learning (VFL), where institutions
collaboratively train machine learning models via combining each other's local
feature or label information, has achieved great success in applications to
financial risk management (FRM). The surging developments of graph
representation learning (GRL) have opened up new opportunities for FRM
applications under FL via efficiently utilizing the graph-structured data
generated from underlying transaction networks. Meanwhile, transaction
information is often considered highly sensitive. To prevent data leakage
during training, it is critical to develop FL protocols with formal privacy
guarantees. In this paper, we present an end-to-end GRL framework in the VFL
setting called VESPER, which is built upon a general privatization scheme
termed perturbed message passing (PMP) that allows the privatization of many
popular graph neural architectures.Based on PMP, we discuss the strengths and
weaknesses of specific design choices of concrete graph neural architectures
and provide solutions and improvements for both dense and sparse graphs.
Extensive empirical evaluations over both public datasets and an industry
dataset demonstrate that VESPER is capable of training high-performance GNN
models over both sparse and dense graphs under reasonable privacy budgets.

本文介绍了一种基于 VFL 框架的端到端 GRL 方法，称为 VESPER，它建立在一种通用的隐私保护方案（PMP）上，允许对许多常见的图神经网络架构进行隐私保护。通过对具体图神经网络架构的设计选择进行讨论，我们提出了针对稠密图和稀疏图的解决方案和改进方法。对公共数据集和行业数据集的大量实证评估表明，VESPER 能够在合理的隐私预算下训练高性能的 GNN 模型。