CFTC chairman Michael Selig sat down with WIRED to discuss how the agency scours Polymarket and other prediction markets for illegal activity. For most of the past year, it looked like prediction markets had kicked off a new golden age of fraud. On Polymarket, traders raked in fortunes from suspiciously timed bets on geopolitical events like the raid on Venezuela and the Iran War. It wasn't clear whether the US government would bother pursuing some of the most flagrant bad actors, since Polymarket's crypto-based platform was technically offshore and not regulated or licensed within the country. Now, however, the Commodity Futures Trading Commission, which oversees prediction markets, wants you to know that it's watching very, very closely.

Mark Moran, an underdog Senate candidate from Virginia, claims he wanted to get caught violating the prediction market platform's rules. Kalshi announced Wednesday that it had taken action against three US politicians for violating the prediction market platform's rules on insider trading. One of the candidates, Mark Moran, a former investment banker and contestant on the reality dating show, is running a long-shot campaign for US Senate in Virginia against incumbent Mark Warner. According to Moran, getting caught was actually his plan all along: "I bet $100 on myself, not denying that, I did do it," he tells WIRED. "I wanted to see if they would enforce it."

This study explores the application of generative adversarial networks in financial market supervision, especially for solving the problem of data imbalance to improve the accuracy of risk prediction. Since financial market data are often imbalanced, especially high-risk events such as market manipulation and systemic risk occur less frequently, traditional models have difficulty effectively identifying these minority events. This study proposes to generate synthetic data with similar characteristics to these minority events through GAN to balance the dataset, thereby improving the prediction performance of the model in financial supervision. Experimental results show that compared with traditional oversampling and undersampling methods, the data generated by GAN has significant advantages in dealing with imbalance problems and improving the prediction accuracy of the model. This method has broad application potential in financial regulatory agencies such as the U.S. Securities and Exchange Commission (SEC), the Financial Industry Regulatory Authority (FINRA), the Federal Deposit Insurance Corporation (FDIC), and the Federal Reserve.

As algorithmic trading and electronic markets continue to transform the landscape of financial markets, detecting and deterring rogue agents to maintain a fair and efficient marketplace is crucial. The explosion of large datasets and the continually changing tricks of the trade make it difficult to adapt to new market conditions and detect bad actors. To that end, we propose a framework that can be adapted easily to various problems in the space of detecting market manipulation. Our approach entails initially employing a labelling algorithm which we use to create a training set to learn a weakly supervised model to identify potentially suspicious sequences of order book states. The main goal here is to learn a representation of the order book that can be used to easily compare future events. Subsequently, we posit the incorporation of expert assessment to scrutinize specific flagged order book states. In the event of an expert's unavailability, recourse is taken to the application of a more complex algorithm on the identified suspicious order book states. We then conduct a similarity search between any new representation of the order book against the expert labelled representations to rank the results of the weak learner. We show some preliminary results that are promising to explore further in this direction

The intersection of social media, low-cost trading platforms, and naive investors has created an ideal situation for information-based market manipulations, especially pump&dumps. Manipulators accumulate small-cap stocks, disseminate false information on social media to inflate their price, and sell at the peak. We collect a dataset of stocks whose price and volume profiles have the characteristic shape of a pump&dump, and social media posts for those same stocks that match the timing of the initial price rises. From these we build predictive models for pump&dump events based on the language used in the social media posts. There are multiple difficulties: not every post will cause the intended market reaction, some pump&dump events may be triggered by posts in other forums, and there may be accidental confluences of post timing and market movements. Nevertheless, our best model achieves a prediction accuracy of 85% and an F1-score of 62%. Such a tool can provide early warning to investors and regulators that a pump&dump may be underway.

Important information: This is a sponsored story. Please remember that the value of investments, and any income from them, can fall as well as rise so you could get back less than you invest. If you are unsure of the suitability of your investment please seek advice. As we all know, AI technology is becoming more prominent in many areas, such as navigation,entertainment and software development. With each passing day, more and more people gain access to this technology and employ it in more areas, and of course, the economy couldn't be the exception.

The Nugenesis team is currently working on making the system available to its stakers and users. NuGenesis market analysis and prediction tools are expected to be live by mid-2024. Nugenesis will continue refining and developing its technology, with the eventual goal of providing live predictions and analysis for all major markets. NAVIS MAMS (market analytics and monitoring systems) has been in the machine learning stage for over 12 months, with the first successful pattern to predict crypto prices in as little as three months. Navis data points were changed to focus on social influence and market manipulation, as market data alone was deemed insufficient.

The team at Tel Aviv-based Shield, an established Regtech firm, reveals that they're introducing their powerful Alert Transparency capabilities, bringing "unmatched" understanding to compliance alerts and triggers via AI, Natural Language Processing (NLP), and various other backend technologies. As regulatory authorities throughout the world aim to define and understand AI's growing role within financial institutions and AI-powered Fintech companies struggle to provide "true" transparency due to their proprietary "black box" solutions, Alert Transparency offers key insights into "why an alert was triggered so financial organizations can detect possible market manipulations, which has been particularly prevalent in today's new hybrid work from home environment." "With the already proven ability to automate surveillance through its award-winning artificial intelligence platform, Shield's Alert Transparency provides compliance teams with an in-depth analysis and understanding of communication triggers, including the scenario, the rule that was compromised, and an overall relevancy score." Since regulatory guidelines and related procedures tend to vary based on the specific financial organization, as well as how and where they do business, compliance officers are able "to customize what triggers an alert to the specific needs of their company," the announcement from Shield noted. A complete Workplace Intelligence platform, Shield's Alert Transparency is able "to pinpoint various risks across communication channels, including insider trading, spoofing, front-running, and even sexual harassment and racism," the update from Shield explained.

A dedicated writer and digital evangelist. Are you aware of how the buying and selling of stocks were carried out when there was no internet or computers? Back then, stock exchanges had active trading floors filled with brokers and traders. To make a trade or a purchase, they had to shout or use hand signals to alert others about their buy or sell orders. It looked a whole lot like an auction at a fish market today. But then came computers and the internet to change the game completely.

Surrounded by rallies of "power to the people," a rag-tag group of scrappy underdogs recently managed to bring Wall Street to its knees through a dazzling display of disobedient investing that saw Gamestop stocks rocket Moonward. This unprecedented seizure of power by the proletariat has been lauded far and wide as a smack in the mouth for the establishment. Some say it's a warning shot to the financial kings and queens of the Earth. The "Gamestonk" legend will be told for years to come – Hollywood's already making sure of that. But the story is far from done.