Financial Technology: Overviews

Emerging trends in Financial Services & FinTech: Artificial Intelligence, Machine Learning to define future


Two major trends Artificial Intelligence and Machine Learning are going to define the future of fintechs, said Soumya Rajan, Founder & CEO, Waterfield Advisors, at the FE Modern BFSI Summit. As for the emerging trends in the financial sector, Rajan noted two big themes, connectivity and computing, which are going to shape up the future. As far as connectivity is concerned, India has 750 million smartphone users, which is likely to become 1 billion by 2026. Rajan said that on the demographics front, the Gen Ys, and the Gen Zs are digital natives, which rely more on the technology for their financial services. In 2021, around 770 billion digital transactions happened globally, of which around 40 billion were with regard to mobile money.

AI in the Canadian Financial Services Industry


In recent years, players within Canada's financial services industry, from banks to Fintech startups, have shown early and innovative adoption of artificial intelligence ("AI") and machine learning ("ML") within their organizations and services. With the ability to review and analyze vast amounts of data, AI algorithms and ML help financial services organizations improve operations, safeguard against financial crime, sharpen their competitive edge and better personalize their services. As the industry continues to implement more AI and build upon its existing applications, it should ensure that such systems are used responsibly and designed to account for any unintended consequences. Below we provide a brief overview of current considerations, as well as anticipated future shifts, in respect of the use of AI in Canada's financial services industry. At a high level, Canadian banks and many bank-specific activities are matters of federal jurisdiction.

Artificial Intelligence and Blockchain at the Heart of Modern Tech Evolution


Many things have changed since the beginning of the 21st century. At the core of all these transformations, a simple concept called'technology' prevails. Yes, for the past two decades, technology has affected the way we live, work, learn, study, communicate, transport, and even think. As a result of modern trends' intrusion, computers are becoming faster, more portable, and higher-powered than ever before. Although the tech evolution has both positive and negative impacts, the good side of the transformation is heavily admired by people.

2022 technology trend review, part one: Open source, cloud, blockchain


In the spirit of the last couple of years, we review developments in what we have identified as the key technology drivers for the 2020s in the world of databases, data management, and AI. We are looking back at 2021, trying to identify patterns that will shape 2022. We start today with a review of open source software, the cloud, and blockchain. We will continue in the coming days with a review of AI and knowledge graphs. Open source software has been on the rise for a while, and we don't see any signs of this growth slowing down.

Edain Technologies -- Comprehensive Review


The pandemic situation has changed the way we do business. Digitalization has accelerated in many ways. We see many businesses moving to online, we see cryptocurrencies emerging and metaverses being created. With Facebook's recent announcement of rebranding to Meta, this trend has only gained speed. Artificial intelligence (AI) has now become more important than ever.

Blockchain-based Federated Learning: A Comprehensive Survey Artificial Intelligence

With the technological advances in machine learning, effective ways are available to process the huge amount of data generated in real life. However, issues of privacy and scalability will constrain the development of machine learning. Federated learning (FL) can prevent privacy leakage by assigning training tasks to multiple clients, thus separating the central server from the local devices. However, FL still suffers from shortcomings such as single-point-failure and malicious data. The emergence of blockchain provides a secure and efficient solution for the deployment of FL. In this paper, we conduct a comprehensive survey of the literature on blockchained FL (BCFL). First, we investigate how blockchain can be applied to federal learning from the perspective of system composition. Then, we analyze the concrete functions of BCFL from the perspective of mechanism design and illustrate what problems blockchain addresses specifically for FL. We also survey the applications of BCFL in reality. Finally, we discuss some challenges and future research directions.

AI in Finance: Challenges, Techniques and Opportunities Artificial Intelligence

AI in finance broadly refers to the applications of AI techniques in financial businesses. This area has been lasting for decades with both classic and modern AI techniques applied to increasingly broader areas of finance, economy and society. In contrast to either discussing the problems, aspects and opportunities of finance that have benefited from specific AI techniques and in particular some new-generation AI and data science (AIDS) areas or reviewing the progress of applying specific techniques to resolving certain financial problems, this review offers a comprehensive and dense roadmap of the overwhelming challenges, techniques and opportunities of AI research in finance over the past decades. The landscapes and challenges of financial businesses and data are firstly outlined, followed by a comprehensive categorization and a dense overview of the decades of AI research in finance. We then structure and illustrate the data-driven analytics and learning of financial businesses and data. The comparison, criticism and discussion of classic vs. modern AI techniques for finance are followed. Lastly, open issues and opportunities address future AI-empowered finance and finance-motivated AI research.

Artificial Intelligence (AI) In Fintech Market Growth by Top Companies, Region, Application, Driver, Trends and Forecasts by 2027 – Crypto Daily


The Artificial Intelligence (AI) In Fintech Market report predicts promising growth and development during the period 2020-2027. The Artificial Intelligence (AI) In Fintech Market survey report represents vital statistical data represented in an organized format such as graphs, charts, tables, and figures to provide a detailed understanding of the Artificial Intelligence (AI) In Fintech Market in a simple manner. The report covers an in-depth analysis of the Artificial Intelligence (AI) In Fintech market and offers key insights on current and emerging trends, market drivers, and market insights offered by industry experts. The report examines the impact of COVID-19 on market growth. The study provides comprehensive coverage of the impact of the COVID-19 pandemic on the Artificial Intelligence (AI) In Fintech market and its key segments.

How AI And Data Analytics Are Shaping The Future Of Fintech - The largest technology publication on emerging trends


Historically, the fintech industry has been among the earliest Artificial Intelligence adopters. As of today, AI is becoming the main driver of digital transformation in traditional finance and the golden standard for fintech services. In fact, according to a recently published report authorized by the World Economic Forum and conducted in partnership with the Cambridge Centre for Alternative Finance, by 2022, we can expect mass adoption of AI in the financial industry on a global scale. In other ways, legacy financial services will become obsolete in as little as two years. AI and Data analytics go hand in hand, and nascent technologies like Machine Learning, Neural Networks, and Natural Language Processing, continue to improve data-crunching capabilities for financial industry players.

AI in FinTech: A Research Agenda Artificial Intelligence

Smart FinTech has emerged as a new area that synthesizes and transforms AI and finance, and broadly data science, machine learning, economics, etc. Smart FinTech also transforms and drives new economic and financial businesses, services and systems, and plays an increasingly important role in economy, technology and society transformation. This article presents a highly summarized research overview of smart FinTech, including FinTech businesses and challenges, various FinTech-associated data and repositories, FinTech-driven business decision and optimization, areas in smart FinTech, and research methods and techniques for smart FinTech.