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Finetuned large language models (LLMs) have shown remarkable performance in financial tasks, such as sentiment analysis and information retrieval. Due to privacy concerns, finetuning and deploying financial LLMs (FinLLMs) locally are crucial for institutions and individuals. In this paper, we employ quantized low-rank adaptation (QLoRA) to finetune FinLLMs, which leverage low-rank structure and quantization technique to significantly reduce computational requirements while maintaining model performance. We also employ data and pipeline parallelism to enable local finetuning on commodity GPUs. Experiments on financial datasets validate the efficacy of our approach in yielding notable improvements over the base models. 

Financial large language models (FinLLMs) have been applied to various tasks in business, finance, accounting, and auditing. Complex financial regulations and standards are critical to financial services, which LLMs must comply with. However, FinLLMs’ performance in understanding and interpreting financial regulations has rarely been studied. Therefore, we organize the Regulations Challenge, a shared task at COLING FinNLP-FNP-LLMFinLegal2025. It encourages the academic community to explore the strengths and limitations of popular LLMs. We create 9 novel tasks and corresponding question sets. In this paper, we provide an overview of these tasks and summarize participants’ approaches and results. We aim to raise awareness of FinLLMs’ professional capability in financial regulations. 

The global financial landscape is experiencing significant transformation driven by technological advancements and evolving market dynamics. Moreover, blockchain technology has become a pivotal platform with widespread applications, especially in finance. Cross-border payments have emerged as a key area of interest, with blockchain offering inherent benefits such as enhanced security, transparency, and efficiency compared to traditional banking systems. This paper presents a novel framework leveraging blockchain technology and smart contracts to emulate cross-border payments, ensuring interoperability and compliance with international standards such as ISO20022. Key contributions of this paper include a novel prototype framework for implementing smart contracts and web clients for streamlined transactions and a mechanism to translate ISO20022 standard messages. Our framework can provide a practical solution for secure, efficient, and transparent cross-border transactions, contributing to the ongoing evolution of global finance and the emerging landscape of decentralized finance.