The banking sector, as a data-driven industry, relies on the availability of high-quality data to create value and protect its customers. The synergy between recent deep learning (DL) advancements, and the sector's data needs presents a growth potential of USD$4.6 trillion by 2035 (Accenture, 2017). However, deploying DL models is challenging due to the need for large, high-quality training data (Ryll et al., 2020), a difficulty made worse by the intricacy of financial transaction data (with complex data patterns and time-related characteristics), and strict regulations that limit data sharing (EU Regulation 2016/679, PCI DSS v4.0). One possible solution is to use synthetic data which is artificially generated rather than drawn from real-world events to increase samples in the minority class (Jordon et al., 2022), and allow safe data sharing between financial institutions while protecting privacy (Karst et al., 2024). This approach is essential for improving models used in assessing risks and detecting fraud.

In an era where digital security is crucial, efficient processing of security-related inquiries through supply chain security questionnaires is imperative. This paper introduces a novel approach using Natural Language Processing (NLP) and Retrieval-Augmented Generation (RAG) to automate these responses. We developed QuestSecure, a system that interprets diverse document formats and generates precise responses by integrating large language models (LLMs) with an advanced retrieval system. Our experiments show that QuestSecure significantly improves response accuracy and operational efficiency. By employing advanced NLP techniques and tailored retrieval mechanisms, the system consistently produces contextually relevant and semantically rich responses, reducing cognitive load on security teams and minimizing potential errors. This research offers promising avenues for automating complex security management tasks, enhancing organizational security processes.

Recommendation systems are ubiquitous, from Spotify playlist suggestions to Amazon product suggestions. Nevertheless, depending on the methodology or the dataset, these systems typically fail to capture user preferences and generate general recommendations. Recent advancements in Large Language Models (LLM) offer promising results for analyzing user queries. However, employing these models to capture user preferences and efficiency remains an open question. In this paper, we propose LLMRS, an LLM-based zero-shot recommender system where we employ pre-trained LLM to encode user reviews into a review score and generate user-tailored recommendations. We experimented with LLMRS on a real-world dataset, the Amazon product reviews, for software purchase use cases. The results show that LLMRS outperforms the ranking-based baseline model while successfully capturing meaningful information from product reviews, thereby providing more reliable recommendations.