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Financial Technology: Overviews


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

ZDNet

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

#artificialintelligence

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

arXiv.org 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

arXiv.org 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 in Fintech Market Size and Growth Opportunities with COVID19 Impact Analysis

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Artificial Intelligence in Fintech Market Size and Forecast 2021-2028 by Verified Market Research specialize in market strategy, market direction, expert opinions, and knowledgeable insight into the global market. The report is a combination of critical information including the competitive landscape; global, regional, and country-specific market size; Market participants; Market growth analysis; Market share; Analysis of opportunities, recent developments, and growth in segmentation. The report also provides other information and thoughtful facts such as historical data, sales, revenue and global market share of Artificial Intelligence in Fintech, product scope, market overview, opportunities, driving force and market share of Artificial Intelligence in Fintech. One of the important factors that make this report interesting is its comprehensive overview of the industry's competitive landscape. The report includes upstream raw materials and downstream needs analyses.


Smart and Secure CAV Networks Empowered by AI-Enabled Blockchain: Next Frontier for Intelligent Safe-Driving Assessment

arXiv.org Artificial Intelligence

Securing a safe-driving circumstance for connected and autonomous vehicles (CAVs) continues to be a widespread concern despite various sophisticated functions delivered by artificial intelligence for in-vehicle devices. Besides, diverse malicious network attacks become ubiquitous along with the worldwide implementation of the Internet of Vehicles, which exposes a range of reliability and privacy threats for managing data in CAV networks. Combined with another fact that CAVs are now limited in handling intensive computation tasks, it thus renders a pressing demand of designing an efficient assessment system to guarantee autonomous driving safety without compromising data security. To this end, we propose in this article a novel framework of Blockchain-enabled intElligent Safe-driving assessmenT (BEST) to offer a smart and reliable approach for conducting safe driving supervision while protecting vehicular information. Specifically, a promising solution of exploiting a long short-term memory algorithm is first introduced in detail for an intElligent Safe-driving assessmenT (EST) scheme. To further facilitate the EST, we demonstrate how a distributed blockchain obtains adequate efficiency, trustworthiness and resilience with an adopted byzantine fault tolerance-based delegated proof-of-stake consensus mechanism. Moreover, several challenges and discussions regarding the future research of this BEST architecture are presented.


Graph Computing for Financial Crime and Fraud Detection: Trends, Challenges and Outlook

arXiv.org Artificial Intelligence

The rise of digital payments has caused consequential changes in the financial crime landscape. As a result, traditional fraud detection approaches such as rule-based systems have largely become ineffective. AI and machine learning solutions using graph computing principles have gained significant interest in recent years. Graph-based techniques provide unique solution opportunities for financial crime detection. However, implementing such solutions at industrial-scale in real-time financial transaction processing systems has brought numerous application challenges to light. In this paper, we discuss the implementation difficulties current and next-generation graph solutions face. Furthermore, financial crime and digital payments trends indicate emerging challenges in the continued effectiveness of the detection techniques. We analyze the threat landscape and argue that it provides key insights for developing graph-based solutions.


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

#artificialintelligence

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

#artificialintelligence

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 to transform global banking, execs say - Fintech News

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Artificial intelligence (AI) and machine learning will shape the global banking industry over the next five years, according to a survey of industry executives. The research, conducted by the Economist Intelligence Unit in partnership with software company Temenos, found that more than three quarters (77%) of the 305 banking executives questioned thought AI would be a key differentiator between successful and unsuccessful banks in the next few years. In particular, AI was set to improve the customer experience, respondents agreed, with 28% saying this would be a key use for the technology. Two thirds (66%) of respondents said new technologies such as AI would drive the development of the global banking industry over the next five years, compared to 42% who agreed with this when asked last year. Separate research published in February by Juniper reported that AI and similar technologies were "crucial" to fighting fraud as online scams became more advanced.