Retrieval-Augmented Generation (RAG) systems often face limitations in specialized domains such as fintech, where domain-specific ontologies, dense terminology, and acronyms complicate effective retrieval and synthesis. This paper introduces an agentic RAG architecture designed to address these challenges through a modular pipeline of specialized agents. The proposed system supports intelligent query reformulation, iterative sub-query decomposition guided by keyphrase extraction, contextual acronym resolution, and cross-encoder-based context re-ranking. We evaluate our approach against a standard RAG baseline using a curated dataset of 85 question--answer--reference triples derived from an enterprise fintech knowledge base. Experimental results demonstrate that the agentic RAG system outperforms the baseline in retrieval precision and relevance, albeit with increased latency. These findings suggest that structured, multi-agent methodologies offer a promising direction for enhancing retrieval robustness in complex, domain-specific settings.

Identifying and then implementing an effective response to disruptive new AI technologies is enormously challenging for any business looking to integrate AI into their operations, as well as regulators looking to leverage AI-related innovation as a mechanism for achieving regional economic growth. These business and regulatory challenges are particularly significant given the broad reach of AI, as well as the multiple uncertainties surrounding such technologies and their future development and effects. This article identifies two promising strategies for meeting the AI challenge, focusing on the example of Fintech. First, dynamic regulation, in the form of regulatory sandboxes and other regulatory approaches that aim to provide a space for responsible AI-related innovation. An empirical study provides preliminary evidence to suggest that jurisdictions that adopt a more proactive approach to Fintech regulation can attract greater investment. The second strategy relates to so-called innovation ecosystems. It is argued that such ecosystems are most effective when they afford opportunities for creative partnerships between well-established corporations and AI-focused startups and that this aspect of a successful innovation ecosystem is often overlooked in the existing discussion. The article suggests that these two strategies are interconnected, in that greater investment is an important element in both fostering and signaling a well-functioning innovation ecosystem and that a well-functioning ecosystem will, in turn, attract more funding. The resulting synergies between these strategies can, therefore, provide a jurisdiction with a competitive edge in becoming a regional hub for AI-related activity.

The rapid evolution of the Smart-everything movement and Artificial Intelligence (AI) advancements have given rise to sophisticated cyber threats that traditional methods cannot counteract. Cyber threats are extremely critical in financial technology (FinTech) as a data-centric sector expected to provide 24/7 services. This paper introduces a novel and refined taxonomy of security threats in FinTech and conducts a comprehensive systematic review of defensive strategies. Through PRISMA methodology applied to 74 selected studies and topic modeling, we identified 11 central cyber threats, with 43 papers detailing them, and pinpointed 9 corresponding defense strategies, as covered in 31 papers. This in-depth analysis offers invaluable insights for stakeholders ranging from banks and enterprises to global governmental bodies, highlighting both the current challenges in FinTech and effective countermeasures, as well as directions for future research.

The Association for the Advancement of Artificial Intelligence's Inaugural Summer Symposium Series was held held at Singapore EXPO in Singapore, July 17-19, 2023. There were five symposia in the summer program: Second Symposium on Human Partnership with Medical AI: Design, Operationalization, and Ethics, AI x Metaverse, Building Connections: From Human-Human to Human-AI Collaboration, Artificial Intelligence for FinTech (AI4FinTech), and Embodied Intelligence. Building on the success of the inaugural symposium held in 2021, the second symposium on Human Partnership with Medical AI delved deeper into the critical components of Trust, Ethics, and Security in the design and operationalization of Clinical AI. This year, the event aimed to continue the discussions and collaborations that started in the previous symposium and explore new avenues of clinical utility, trustworthiness, robustness, and responsible AI. The symposium brought together researchers, clinicians, policymakers, and stakeholders from various domains to discuss the challenges and opportunities of AI-human partnership, share their latest research and insights, and develop actionable strategies to create trustworthy, ethical, and secure AI systems.

Applications of Reinforcement Learning in the Finance Technology (Fintech) have acquired a lot of admiration lately. Undoubtedly Reinforcement Learning, through its vast competence and proficiency, has aided remarkable results in the field of Fintech. The objective of this systematic survey is to perform an exploratory study on a correlation between reinforcement learning and Fintech to highlight the prediction accuracy, complexity, scalability, risks, profitability and performance. Major uses of reinforcement learning in finance or Fintech include portfolio optimization, credit risk reduction, investment capital management, profit maximization, effective recommendation systems, and better price setting strategies. Several studies have addressed the actual contribution of reinforcement learning to the performance of financial institutions. The latest studies included in this survey are publications from 2018 onward. The survey is conducted using PRISMA technique which focuses on the reporting of reviews and is based on a checklist and four-phase flow diagram. The conducted survey indicates that the performance of RL-based strategies in Fintech fields proves to perform considerably better than other state-of-the-art algorithms. The present work discusses the use of reinforcement learning algorithms in diverse decision-making challenges in Fintech and concludes that the organizations dealing with finance can benefit greatly from Robo-advising, smart order channelling, market making, hedging and options pricing, portfolio optimization, and optimal execution.

As the calendar turns to 2023, Intuit's leading technologists share the trends they'll be watching closely. Ranging from the generative AI phenomenon, the rise in data protection regulations, and implications of Web3 for the fintech landscape, to how "thinking like criminals" will pay off for companies looking to stave off bad actors, Intuit leaders weigh in on what the future holds. Opportunities to catalyze innovation abound for tech companies. Generative AI is rapidly becoming more powerful and more prominent, popularized by chatbots and apps such as ChatGPT and Lensa, but it still needs to develop and mature before it can safely be used in industries where the accuracy of statements are critical, such as finance or medicine. Within the next several years, generative AI will likely play a pivotal role in helping create personalized conversational systems to provide financial or medical advice and guidance directly to customers.

AI is already changing the way financial firms operate, and that change is only going to accelerate. What single change would financial services executives wish for from one wave of a magic wand? Broadridge posed that question to the C-suite executives participating in its latest Digital Transformation and Next-Gen Tech Study. The result was a clear: more artificial intelligence. AI is already changing the way financial services companies operate, and that transformation is poised to accelerate as firms target further expansion and integration of AI as a top strategic priority.

Artificial Intelligence is creating a buzzword with a significant aspect in the Finance sector. The financial sector around the world is trying to adopt & implement AI in its finance service capabilities. Exponential growth in the finance sector is measured in the last few years using Predictive Analysis. AI/machine learning technologies are helping bank business services to engage their potential customers. The rising popularity of messaging apps and the higher demands of customers in the banking, health, or wellness industry is giving chatbots a boost.

The fintech industry is constantly evolving and improving, and 2023 is no exception. It is predicted that the fintech space will multiply and reach $174 billion in 2023. The banking industry has recently experienced a massive surge in adopting fintech solutions as users become more tech-savvy. There are several emerging technologies in Fintech, and I have highlighted some of the most predominant ones that will be witnessed in 2023 below. AI and ML can transform investments, payments, banking, risk management, and related industries.

Optasia is a fully-integrated B2B2X financial technology platform covering scoring, financial decisioning, disbursement & collection. We provide a versatile AI Platform powering financial inclusion, delivering responsible financing decision-making and driving a superior business model & strong customer experience with presence in 30 Countries anchored by 7 Regional Offices. We are seeking for enthusiastic professionals, with energy, who are results driven and have can-do attitude, who want to be part of a team of likeminded individuals who are delivering solutions in an innovative and exciting environment. Data Scientists are significant contributors of Optasia's advanced risk management and revenue optimization. As member of the Data Science team in Optasia, you will have an opportunity to combine the disciplines of risk management, research, and technology to operate trading strategies across multiple projects.