The SuperCLUE-Fin (SC-Fin) benchmark is a pioneering evaluation framework tailored for Chinese-native financial large language models (FLMs). It assesses FLMs across six financial application domains and twenty-five specialized tasks, encompassing theoretical knowledge and practical applications such as compliance, risk management, and investment analysis. Using multi-turn, open-ended conversations that mimic real-life scenarios, SC-Fin measures models on a range of criteria, including accurate financial understanding, logical reasoning, clarity, computational efficiency, business acumen, risk perception, and compliance with Chinese regulations. In a rigorous evaluation involving over a thousand questions, SC-Fin identifies a performance hierarchy where domestic models like GLM-4 and MoonShot-v1-128k outperform others with an A-grade, highlighting the potential for further development in transforming theoretical knowledge into pragmatic financial solutions. This benchmark serves as a critical tool for refining FLMs in the Chinese context, directing improvements in financial knowledge databases, standardizing financial interpretations, and promoting models that prioritize compliance, risk management, and secure practices. We create a contextually relevant and comprehensive benchmark that drives the development of AI in the Chinese financial sector. SC-Fin facilitates the advancement and responsible deployment of FLMs, offering valuable insights for enhancing model performance and usability for both individual and institutional users in the Chinese market..~\footnote{Our benchmark can be found at \url{https://www.CLUEbenchmarks.com}}.

Given the increasing compliance demands on organisations from the public and regulators alike, companies cannot afford to neglect their compliance obligations. The role of compliance is to prevent, detect, respond to and remediate risk and financial institutions (FIs) must use all of the tools at their disposal to achieve it. Implementing an effective compliance framework requires the whole firm to be on board, from the C-suite down. The compliance function is in a state of flux, however – virtually unrecognisable from a decade ago. The catalyst for much of the change in the financial services industry was the global financial crisis.

Machine learning and artificial intelligence have become buzzwords in the financial services industry. Daniel Fernandez helps to break down the difference between the two terms and explains how these technologies are being used by compliance departments today. By delving into how these technologies work, Daniel sheds light on the issues they can help to solve, the challenges facing their increased adoption and what financial institutions should be doing right now to take advantage. Like many industry buzzwords, artificial intelligence (AI) has become a hot topic that RegTech technologists often write or speak about. But the reality is this: AI has become an overloaded and misused term, often mistaken for machine learning (ML).

Like many industry buzzwords, Artificial Intelligence (AI) has become a hot topic that RegTech technologists often write or speak about. But the reality is – AI has become an overloaded and misused term, often mistaken for Machine Learning (ML). This blog aims to clarify the difference between the two, explain some of the complexities of implementing these solutions today, and highlight how ML can immediately add value in financial compliance applications. In simple terms, Artificial Intelligence enables computer systems to perform tasks that require human intelligence. Intelligence is the key word.

A U.K. startup that uses artificial intelligence to help banks and other financial firms with anti-money laundering compliance received $8.2 million to fund its expansion in Europe and North America. The financing for London-based ComplyAdvantage is being lead by British venture capital firm Balderton Capital. The company, which said in a statement it has 200 clients globally, said it would use the money to expand its operations, adding to its current team of about 50 employees. It will also open a sales office in New York this week. Complying with the growing body of anti-money laundering and know-your-customer regulations around the world is becoming an expensive headache for financial institutions.