Abstract--The complexity and inter-connectivity of entities involved in money laundering demand investigative reasoning over graph-structured data. This paper explores the use of large language models (LLMs) as reasoning engines over localized subgraphs extracted from a financial knowledge graph. We propose a lightweight pipeline that retrieves k-hop neighborhoods around entities of interest, serializes them into structured text, and prompts an LLM via few-shot in-context learning to assess suspiciousness and generate justifications. Using synthetic anti-money laundering (AML) scenarios that reflect common laundering behaviors, we show that LLMs can emulate analyst-style logic, highlight red flags, and provide coherent explanations. While this study is exploratory, it illustrates the potential of LLM-based graph reasoning in AML and lays groundwork for explainable, language-driven financial crime analytics.

In the rapidly evolving domain of financial technology, the detection of illicit transactions within blockchain networks remains a critical challenge, necessitating robust and innovative solutions. This work proposes a novel approach by combining Quantum Inspired Graph Neural Networks (QI-GNN) with flexibility of choice of an Ensemble Model using QBoost or a classic model such as Random Forrest Classifier. This system is tailored specifically for blockchain network analysis in anti-money laundering (AML) efforts. Our methodology to design this system incorporates a novel component, a Canonical Polyadic (CP) decomposition layer within the graph neural network framework, enhancing its capability to process and analyze complex data structures efficiently. Our technical approach has undergone rigorous evaluation against classical machine learning implementations, achieving an F2 score of 74.8% in detecting fraudulent transactions. These results highlight the potential of quantum-inspired techniques, supplemented by the structural advancements of the CP layer, to not only match but potentially exceed traditional methods in complex network analysis for financial security. The findings advocate for a broader adoption and further exploration of quantum-inspired algorithms within the financial sector to effectively combat fraud.

Combating money laundering has become increasingly complex with the rise of cybercrime and digitalization of financial transactions. Graph-based machine learning techniques have emerged as promising tools for Anti-Money Laundering (AML) detection, capturing intricate relationships within money laundering networks. However, the effectiveness of AML solutions is hindered by data silos within financial institutions, limiting collaboration and overall efficacy. This research presents a novel privacy-preserving approach for collaborative AML machine learning, facilitating secure data sharing across institutions and borders while preserving privacy and regulatory compliance. Leveraging Fully Homomorphic Encryption (FHE), computations are directly performed on encrypted data, ensuring the confidentiality of financial data. Notably, FHE over the Torus (TFHE) was integrated with graph-based machine learning using Zama Concrete ML. The research contributes two key privacy-preserving pipelines. First, the development of a privacy-preserving Graph Neural Network (GNN) pipeline was explored. Optimization techniques like quantization and pruning were used to render the GNN FHE-compatible. Second, a privacy-preserving graph-based XGBoost pipeline leveraging Graph Feature Preprocessor (GFP) was successfully developed. Experiments demonstrated strong predictive performance, with the XGBoost model consistently achieving over 99% accuracy, F1-score, precision, and recall on the balanced AML dataset in both unencrypted and FHE-encrypted inference settings. On the imbalanced dataset, the incorporation of graph-based features improved the F1-score by 8%. The research highlights the need to balance the trade-off between privacy and computational efficiency.

AI prevails in financial fraud detection and decision making. Yet, due to concerns about biased automated decision making or profiling, regulations mandate that final decisions are made by humans. Financial fraud investigators face the challenge of manually synthesizing vast amounts of unstructured information, including AI alerts, transaction histories, social media insights, and governmental laws. Current Visual Analytics (VA) systems primarily support isolated aspects of this process, such as explaining binary AI alerts and visualizing transaction patterns, thus adding yet another layer of information to the overall complexity. In this work, we propose a framework where the VA system supports decision makers throughout all stages of financial fraud investigation, including data collection, information synthesis, and human criteria iteration. We illustrate how VA can claim a central role in AI-aided decision making, ensuring that human judgment remains in control while minimizing potential biases and labor-intensive tasks.

Money laundering presents a pervasive challenge, burdening society by financing illegal activities. To more effectively combat and detect money laundering, the use of network information is increasingly being explored, exploiting that money laundering necessarily involves interconnected parties. This has lead to a surge in literature on network analytics (NA) for anti-money laundering (AML). The literature, however, is fragmented and a comprehensive overview of existing work is missing. This results in limited understanding of the methods that may be applied and their comparative detection power. Therefore, this paper presents an extensive and systematic review of the literature. We identify and analyse 97 papers in the Web of Science and Scopus databases, resulting in a taxonomy of approaches following the fraud analytics framework of Bockel-Rickermann et al.. Moreover, this paper presents a comprehensive experimental framework to evaluate and compare the performance of prominent NA methods in a uniform setup. The framework is applied on the publicly available Elliptic data set and implements manual feature engineering, random walk-based methods, and deep learning GNNs. We conclude from the results that network analytics increases the predictive power of the AML model with graph neural networks giving the best results. An open source implementation of the experimental framework is provided to facilitate researchers and practitioners to extend upon these results and experiment on proprietary data. As such, we aim to promote a standardised approach towards the analysis and evaluation of network analytics for AML.

Art Law Conference 2023 aims to bring together students, attorneys, artists, arts & legal professionals to discuss contemporary & relevant topics at the intersection of art & law. Speakers will include attorneys & legal professionals from leading law firms in the United States & internationally; artists from diverse backgrounds in modern, contemporary & digital art; & arts professionals including appraisers. Each panel will include an attorney & an artist to dissect the varied subject matter at the intersection of art & law from different perspectives to elucidate artists rights, interest & protection. The panels will include a presentation & discussion from the speakers & conclude with a question & answer session with the audience to ensure interactive engagement. Materials for the conference will include speaker slides & handouts with additional reading materials & resources.

Money laundering is a significant threat to the good functioning of financial systems. Yet there are still little measurable signs of progress in countering money laundering. AML legislation showed to be the least effective of any anti-crime measure. Law enforcement, policymakers, and the media can get so distracted by the immediacy of criminal behavior that it is easy to forget the aim of these illegal activities is not the crime itself, but the proceeds of crime. In this, total failure for AML enforcement is behind the corner.

One subject never fails to light up the eyes of senior bankers and regulators when they're questioned about their efforts to end the money laundering-related scandals that have spread across northern Europe over the last two years: technology. There can be no more damning indictment of the integrity of a bank, or its host nation, than the public revelation that a licensed institution is being used as a laundromat for ill-gotten gains. And what is more enlivening for money-laundering supervisors and bank-compliance officers than showing your firm and country is at the forefront of a technology that could make these troubles disappear? Some of the biggest actors in Europe's financial sector are converts. The UK's Financial Conduct Authority is particularly enthusiastic about using technology to fight money laundering.

Fueled by mobster movies and international espionage thrillers, the phrase has a mysterious, exciting edge to it. But as is often the case, the truth is far less appealing than the glitzy Hollywood version. In reality, money laundering is an activity that traps 40.3 million people in modern slavery, fuels political unrest, and finances terrorism across the globe. Considering the consequences, it's no wonder governments enact AML regulations. And just as money laundering crime grows more sophisticated, so too do the regulations. These regulations have honorable and important intentions, but there's no denying the ever-evolving compliance headaches they create for financial institutions.

RegTech, commonly known as regulatory technology, comprises of all technologies designed to assist companies within the financial industry to comply with regulations. Many of the RegTech solutions primarily focus on anti-money laundering (AML) and know your customer (KYC) regulations. These solutions are designed with high precision levels offered by machine learning technology and artificial intelligence (AI). RegTech solutions cover various functions that include fraud prevention, regulatory change discovery applications, risk management, and KYC. Regulatory changes have increased in volume by almost 500% since the global financial crisis of 2008-09. That could be the reason why the commercial service companies opted to increase the number of their compliance staff and invest more money in their compliance fund.