After two months, Tho Vu was infatuated. The 33-year-old customer service agent, living in Maryland, had met "Ze Zhao" through a dating app, and says she quickly began exchanging messages with him all day on WhatsApp. He seemed like someone she could rely on--he called her "little princess" and sent her reminders to drink enough water. By October 2021, despite never having met in person, they were talking about where to buy a house, how many kids to have, even how he hoped she'd do a home birth. "I want to take you with me when I do anything," he said, in messages seen by TIME.
This week's top reads in banking, fintech, payments, cybersecurity, AI, IoT, risk management and much more In this weeks selection; Top Reads Analysts pin Google retail bank U-turn on fears of higher regulatory scrutiny, low profitability JPMorgan Chase joins UN's Net-Zero Banking Alliance Why Chatbots Fail in Banking We may visit you at home, British financial watchdog warns bank staff SocGen to Cut 3,700 Jobs as Part of Domestic Retail Merger Crypto Could be in Trouble after China Declares all Crypto Transactions Illegal Two Key Digital Payments Trends in the Post-COVID World Capgemini's World Payments Report 2021 Are NFTs a Money Laundering Gold Mine? From tech tool to business asset: How banks are using B2B APIs to fuel growth Will massive outage set back Facebook's payments plans? Analysts pin Google retail bank U-turn on fears of higher regulatory scrutiny, low profitability JPMorgan Chase joins UN's Net-Zero Banking Alliance Why Chatbots Fail in Banking We may visit you at home, British financial watchdog warns bank staff SocGen to Cut 3,700 Jobs as Part of Domestic Retail Merger Crypto Could be in Trouble after China Declares all Crypto Transactions Illegal Two Key Digital Payments Trends in the Post-COVID World Capgemini's World Payments Report 2021 Are NFTs a Money Laundering Gold Mine? From tech tool to business asset: How banks are using B2B APIs to fuel growth Will massive outage set back Facebook's payments plans? JPMorgan Chase joins UN's Net-Zero Banking Alliance Are NFTs a Money Laundering Gold Mine?
An international scam ring is targeting dating app users in a romance scam to not only deprive victims of their cryptocurrency but also the control of their handsets. On Wednesday, Sophos cybersecurity researchers named the gang "CryptoRom" and said they have recently expanded their operations from Asia, spreading to both the United States and Europe. Romance scams are an insidious and constant problem, and thanks to the rising popularity of dating apps, are now not only limited to phishing emails. Instead, fraudsters will'match' with their victims, pretend interest until they build a foundation of trust, and then they will ask for money -- only to vanish soon after. In recent years, romance scams have become more sophisticated, with some cybercriminals offering their victims'exclusivity' in trading deals or in cryptocurrency investments, using the lure of easy profit as well as potential love matches. Interpol warned of an uptick in investment-based romance fraud taking place across dating apps in January this year.
Tóm tắt nội dung--A smart Ponzi scheme is a new form of economic crime that uses Ethereum smart contract account and cryptocurrency to implement Ponzi scheme. The smart Ponzi scheme has harmed the interests of many investors, but researches on smart Ponzi scheme detection is still very limited. The existing smart Ponzi scheme detection methods have the problems of requiring many human resources in feature engineering and poor model portability. To solve these problems, we propose a datadriven smart Ponzi scheme detection system in this paper. The system uses dynamic graph embedding technology to automatically learn the representation of an account based on multi-source and multi-modal data related to account transactions. Compared with traditional methods, the proposed system requires very limited human-computer interaction. To the best of our knowledge, this is the first work to implement smart Ponzi scheme detection through dynamic graph embedding. Ponzi schemes require a constant flow of funds from new investors. The detection method based on source contributed by new investors to pay off the returns of existing code inspection detects the smart Ponzi scheme by manually investors (Figure 1).
Science is a catalyst for human progress. But a publishing monopoly and funding monopsony have inhibited research. We intend to improve incentives in science by developing smart research contracts. These will collectively reward scientific activities, including proposals, papers, replications, datasets, analyses, annotations, editorials, and more. Peer-to-peer review networks will be designed to help evaluate proposals and publications. Long term, these smart contracts help accelerate research by minimizing science friction, ensuring science quality, and maximizing science variance. Papers are the fundamental asset of the research economy: they serve as proof of work that valuable research has been completed. Funding agencies and research institutions evaluate scientists based on their publications. Principal investigators (PIs) attract prospective students and collaborators via papers. Investors and companies use scientific literature to conduct due diligence on research ranging from basic discoveries to clinical studies. Thus, the evaluation and dissemination of papers are vital to this research economy. Publishers are the sole arbiters of papers today. They assign a value -- denominated in "prestige" -- by accepting a paper into the appropriate journal based on selectivity and domain. To evaluate papers, journals typically outsource it to two or three PIs, who often outsource it further to their students. Reviewers are unpaid for this peer review work, as it is an expected part of their scientific duties. Peer review is believed to be necessary because of the industrialization of science. Research papers and proposals have become too specialized and too numerous, making it difficult to assess merit prima facie. As a result, scientific incentives have become distorted in two major ways: prestige capture and reviewer misalignment. Over half of all research papers in 2013 were published by five companies, who have used their centuries of brand equity to build an economic moat. This results in prestige capture, which akin to regulatory capture, causes public and scientific interest to be directed towards the regulators of prestige. Publishers have exploited prestige capture to become the ultimate rent-seekers, with operating margins between 25-40% and market capitalizations up to $50B.
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.
The early 2000s were not a good time for technology. After entering the new millennium amid the impotent panic of the Y2K bug, it wasn't long before the Dotcom Bubble was bursting all the hopes of a new internet-based era. Fortunately the recovery was swift and within a few years brand new technologies were emerging that would transform culture, politics and the economy. They have brought with them new ways of connecting, consuming and getting around, while also raising fresh Doomsday concerns. As we enter a new decade of the 21st Century, we've rounded up the best and worst of the technologies that have taken us here, while offering some clue of where we might be going. There was nothing much really new about the iPhone: there had been phones before, there had been computers before, there had been phones combined into computers before. There was also a lot that wasn't good about it: it was slow, its internet connection barely functioned, and it would be two years before it could even take a video.
Autonomous finance uses AI to make financial decisions on behalf of consumers without the need for direct human input. The service has become especially relevant over the last year as consumers have struggled to maintain financial health during the COVID-19 pandemic. In this episode, Paul Condra, head of emerging technology research, and Robert Le, senior emerging tech analyst, discuss how autonomous finance helps consumers better manage their financial health and performance, as well as the challenges for the technology--including computing costs, consumer trust, regulations and transaction categorization. Listen to all of Season 3 and subscribe to get future episodes of "In Visible Capital" on Apple Podcasts, Spotify, Google Podcasts or wherever you listen. For inquiries, please contact us at email@example.com. Transcript Adam Lewis: Welcome back to "In Visible Capital," a show that discusses the inner workings of the private markets. Today, we'll be sharing a fascinating conversation on autonomous finance from a recent webinar with Paul Condra, our head of emerging tech research and Robert Le, a senior emerging tech analyst who focuses on fintech and insurtech. Adam: Alec, would you believe it if I told you that you could purchase a robot to run your personal finances and wealth management? Alexander: Well, normally, Adam, the skeptic in me would say that that's probably just a little impossible-sounding. The Silicon Valley fintech mavens, you never know what they're going to come up with. The fact is that millions of dollars of venture capital are being bet on apps that can do all of those things and more.
Recent studies in big data analytics and natural language processing develop automatic techniques in analyzing sentiment in the social media information. In addition, the growing user base of social media and the high volume of posts also provide valuable sentiment information to predict the price fluctuation of the cryptocurrency. This research is directed to predicting the volatile price movement of cryptocurrency by analyzing the sentiment in social media and finding the correlation between them. While previous work has been developed to analyze sentiment in English social media posts, we propose a method to identify the sentiment of the Chinese social media posts from the most popular Chinese social media platform Sina-Weibo. We develop the pipeline to capture Weibo posts, describe the creation of the crypto-specific sentiment dictionary, and propose a long short-term memory (LSTM) based recurrent neural network along with the historical cryptocurrency price movement to predict the price trend for future time frames. The conducted experiments demonstrate the proposed approach outperforms the state of the art auto regressive based model by 18.5% in precision and 15.4% in recall.
Social media signals have been successfully used to develop large-scale predictive and anticipatory analytics. For example, forecasting stock market prices and influenza outbreaks. Recently, social data has been explored to forecast price fluctuations of cryptocurrencies, which are a novel disruptive technology with significant political and economic implications. In this paper we leverage and contrast the predictive power of social signals, specifically user behavior and communication patterns, from multiple social platforms GitHub and Reddit to forecast prices for three cyptocurrencies with high developer and community interest - Bitcoin, Ethereum, and Monero. We evaluate the performance of neural network models that rely on long short-term memory units (LSTMs) trained on historical price data and social data against price only LSTMs and baseline autoregressive integrated moving average (ARIMA) models, commonly used to predict stock prices. Our results not only demonstrate that social signals reduce error when forecasting daily coin price, but also show that the language used in comments within the official communities on Reddit (r/Bitcoin, r/Ethereum, and r/Monero) are the best predictors overall. We observe that models are more accurate in forecasting price one day ahead for Bitcoin (4% root mean squared percent error) compared to Ethereum (7%) and Monero (8%).