Per the enforcement action, Coin Cafe will contact eligible customers and begin to refund all fees to U.S.-based investors who request a refund within the next year.

Coin Cafe defrauded hundreds of New Yorkers out of thousands of dollars with its deceptive marketing and due to a lack of effective regulation, she said in a statement. This is yet another example of why the cryptocurrency industry needs to be better regulated, just like any other financial institution where New York investors put their hard-earned money.

In early May, the OAG proposed legislation that would expand its oversight of the crypto industrya move that drew criticism from some onlookers, including New York Rep. Ritchie Torres (D-N.Y.), for threatening the jurisdiction of the states other crypto regulator, the New York Department of Financial Services.

New York operates with a dual financial regulatory system, with the OAG overseeing security and commodity markets, as well as anti-fraud enforcement, and the DFS overseeing banks and bank-like products. In 2015, DFS implemented the BitLicense virtual currency program, the first crypto regulation regime in the nation at a state or federal level.

Coin Cafe applied for a BitLicense in 2015, finally gaining approval in January 2023 while continuing to operate. Under New York law, broker-dealers with a BitLicense still must register with the OAG, which Coin Cafe failed to do.

According to the OAG, before receiving its BitLicense, Coin Cafe claimed to provide investors with free accounts and wallet storage to buy and hold Bitcoin. Starting in September 2020, the company began to charge users for its wallet service without informing them, increasing the fee four times. In the last instance, implemented in October 2022, investors were charged fees equivalent to 96% of their holdings, affecting more than 300 New York customers and wiping out hundreds of investor accounts. Meanwhile, Coin Cafe collected hundreds of thousands of dollars worth of Bitcoin.

When there are inadequate safeguards to protect consumers and investors, James said in the statement, Companies are able to take advantage of New Yorkers.

An OAG investigator opened an account in October 2022, buying $208 worth of Bitcoin over two separate transactions. In March, after Coin Cafe received a BitLicense, Coin Cafe charged the investigator $99 in fees without notice.

The BitLicense program remains a nation-leading example of crypto regulation as lawmakers continue to debate over legislation. Even so, an OAG attorney told Fortune last week that gaps still exist that lead to later enforcement actions. The legislation proposed by James earlier this month would focus more on consumer protection, as well as targeting the type of conflict of interest concerns that have plagued crypto firms like FTX. The bill would also codify the BitLicense into law, which former DFS superintendent Maria Vullo told Fortune would strengthen its authority.

Critics such as Torres argue that the legislation could preempt DFS oversight, while expanding the jurisdictional divides in New Yorks dual regulatory system, which he said could lead to turf battles.

And this is true in financial markets as much as in sectors like health care, manufacturing and pretty much every other aspect of our lives.

Ive been researching financial markets and algorithmic trading for 14 years. While AI offers lots of benefits, the growing use of these technologies in financial markets also points to potential perils. A look at Wall Streets past efforts to speed up trading by embracing computers and AI offers important lessons on the implications of using them for decision-making.

Program trading fuels Black Monday

In the early 1980s, fueled by advancements in technology and financial innovations such as derivatives, institutional investors began using computer programs to execute trades based on predefined rules and algorithms. This helped them complete large trades quickly and efficiently.

Back then, these algorithms were relatively simple and were primarily used for so-called index arbitrage, which involves trying to profit from discrepancies between the price of a stock index like the S&P 500 and that of the stocks its composed of.

As technology advanced and more data became available, this kind of program trading became increasingly sophisticated, with algorithms able to analyze complex market data and execute trades based on a wide range of factors. These program traders continued to grow in number on the largey unregulated trading freeways on which over a trillion dollars worth of assets change hands every day causing market volatility to increase dramatically.

Eventually this resulted in the massive stock market crash in 1987 known as Black Monday. The Dow Jones Industrial Average suffered what was at the time the biggest percentage drop in its history, and the pain spread throughout the globe.

In response, regulatory authorities implemented a number of measures to restrict the use of program trading, including circuit breakers that halt trading when there are significant market swings and other limits. But despite these measures, program trading continued to grow in popularity in the years following the crash.

HFT: Program trading on steroids

Fast forward 15 years, to 2002, when the New York Stock Exchange introduced a fully automated trading system. As a result, program traders gave way to more sophisticated automations with much more advanced technology: High-frequency trading.

HFT uses computer programs to analyze market data and execute trades at extremely high speeds. Unlike program traders that bought and sold baskets of securities over time to take advantage of an arbitrage opportunity a difference in price of similar securities that can be exploited for profit high-frequency traders use powerful computers and high-speed networks to analyze market data and execute trades at lightning-fast speeds. High-frequency traders can conduct trades in approximately one 64-millionth of a second, compared with the several seconds it took traders in the 1980s.

These trades are typically very short term in nature and may involve buying and selling the same security multiple times in a matter of nanoseconds. AI algorithms analyze large amounts of data in real time and identify patterns and trends that are not immediately apparent to human traders. This helps traders make better decisions and execute trades at a faster pace than would be possible manually.

Another important application of AI in HFT is natural language processing, which involves analyzing and interpreting human language data such as news articles and social media posts. By analyzing this data, traders can gain valuable insights into market sentiment and adjust their trading strategies accordingly.

Benefits of AI trading

These AI-based, high-frequency traders operate very differently than people do.

The human brain is slow, inaccurate and forgetful. It is incapable of quick, high-precision, floating-point arithmetic needed for analyzing huge volumes of data for identifying trade signals. Computers are millions of times faster, with essentially infallible memory, perfect attention and limitless capability for analyzing large volumes of data in split milliseconds.

And, so, just like most technologies, HFT provides several benefits to stock markets.

These traders typically buy and sell assets at prices very close to the market price, which means they dont charge investors high fees. This helps ensure that there are always buyers and sellers in the market, which in turn helps to stabilize prices and reduce the potential for sudden price swings.

High-frequency trading can also help to reduce the impact of market inefficiencies by quickly identifying and exploiting mispricing in the market. For example, HFT algorithms can detect when a particular stock is undervalued or overvalued and execute trades to take advantage of these discrepancies. By doing so, this kind of trading can help to correct market inefficiencies and ensure that assets are priced more accurately.

The downsides

But speed and efficiency can also cause harm.

HFT algorithms can react so quickly to news events and other market signals that they can cause sudden spikes or drops in asset prices.

Additionally, HFT financial firms are able to use their speed and technology to gain an unfair advantage over other traders, further distorting market signals. The volatility created by these extremely sophisticated AI-powered trading beasts led to the so-called flash crash in May 2010, when stocks plunged and then recovered in a matter of minutes erasing and then restoring about $1 trillion in market value.

Since then, volatile markets have become the new normal. In 2016 research, two co-authors and I found that volatility a measure of how rapidly and unpredictably prices move up and down increased significantly after the introduction of HFT.

The speed and efficiency with which high-frequency traders analyze the data mean that even a small change in market conditions can trigger a large number of trades, leading to sudden price swings and increased volatility.

In addition, research I published with several other colleagues in 2021 shows that most high-frequency traders use similar algorithms, which increases the risk of market failure. Thats because as the number of these traders increases in the marketplace, the similarity in these algorithms can lead to similar trading decisions.

This means that all of the high-frequency traders might trade on the same side of the market if their algorithms release similar trading signals. That is, they all might try to sell in case of negative news or buy in case of positive news. If there is no one to take the other side of the trade, markets can fail.

Enter ChatGPT

That brings us to a new world of ChatGPT-powered trading algorithms and similar programs. They could take the problem of too many traders on the same side of a deal and make it even worse.

In general, humans, left to their own devices, will tend to make a diverse range of decisions. But if everyones deriving their decisions from a similar artificial intelligence, this can limit the diversity of opinion.

Consider an extreme, nonfinancial situation in which everyone depends on ChatGPT to decide on the best computer to buy. Consumers are already very prone to herding behavior, in which they tend to buy the same products and models. For example, reviews on Yelp, Amazon and so on motivate consumers to pick among a few top choices.

Since decisions made by the generative AI-powered chatbot are based on past training data, there would be a similarity in the decisions suggested by the chatbot. It is highly likely that ChatGPT would suggest the same brand and model to everyone. This might take herding to a whole new level and could lead to shortages in certain products and service as well as severe price spikes.

This becomes more problematic when the AI making the decisions is informed by biased and incorrect information. AI algorithms can reinforce existing biases when systems are trained on biased, old or limited data sets. And ChatGPT and similar tools have been criticized for making factual errors.

In addition, since market crashes are relatively rare, there isnt much data on them. Since generative AIs depend on data training to learn, their lack of knowledge about them could make them more likely to happen.

For now, at least, it seems most banks wont be allowing their employees to take advantage of ChatGPT and similar tools. Citigroup, Bank of America, Goldman Sachs and several other lenders have already banned their use on trading-room floors, citing privacy concerns.

But I strongly believe banks will eventually embrace generative AI, once they resolve concerns they have with it. The potential gains are too significant to pass up and theres a risk of being left behind by rivals.

But the risks to financial markets, the global economy and everyone are also great, so I hope they tread carefully.

Pawan Jain is Assistant Professor of Finance, West Virginia University.

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Karina Gasparova who worked for the digital trade management solutions firm, essDOCS, from 2019 until her resignation in 2021 told the central London employment tribunal that the XXs in one email referred to kisses, when it was actually a shorthand for points where more information was required.

In the same email, she was found to have misconstrued Alexander Goulandriss excessive use of question marks as being an encrypted way of asking when she would be ready to engage in sexual acts.

Other claims include that when her manager renamed a work file with his initials ajg it was an abbreviation of A Jumbo Genital, and that he was running his hands through his hair and staring at her during a joint business call, in an attempt to chat her up.

Her claims were rejected and described by the judge as a skewed perception of everyday events with Gasparova accused of making extraordinary allegations without evidence and trying to find a sinister motive where it didnt exist.

Nightmare for friendly managers

For managers that are simply trying to be friendly this type of situation is what nightmares are made of. 

Misunderstandings at work happen with alarming regularity, warns Alastair Wallace, the founder of Leadership Hacked who has coached 22,000 executives across 80 companies globally.

Whether its a rushed Slack message, a gesture of the hand on Zoom, or an accidentally explicit typo in an email, he blames the pressure of multi-channel working in a fast-paced working world.

This need for speed creates careless oversights and mistakes that can be easily avoided, he tells Fortune.

Such errors have the power to put the reputation of everyone involved on the line and are, according to the experts, entirely avoidable.

Its not about what you say, its about whats heard

Busy managers frequently communicate from the basis that everyones on the same wavelength and understands whats being said, Carol Evans, entrepreneur, business coach and best-selling author tells Fortune. 

Interpretations of the same message can vary depending on a persons gender, race, upbringing, and so on. Its why as a manager, its so important to know your audience. 

Evans recommends leaders get to know each of their individual workers, down to their personality and their past experiences, to understand how all of this may affect how they actually hear your message. 

Effective communication is not about what you say, its about what they understand, Wallace echoes. 

He suggests leaders ask themselves a few questions before hitting send on any email that could be misconstrued including: What are your key messages? Who is your audience? What do they know about this already? How might they be feeling about this topic?

The same goes for body language. By checking yourself and tailoring the way you deliver your message depending on the recipient (including whether the worker is male or female) its less likely to cause offense.

Messages getting lost in translation is on managers

Training yourself to be purposeful in what you say (and how) to every team member you interact with might seem like an awful lot of fuss when its probably near impossible to control how every interaction will be interpreted.

But the experts consistently told Fortune that the onus on getting messaging correct (or at the very least, attempting to) lies firms on the shoulders of managers and not the other way around. 

Managers have a particular responsibility to ensure that mistakes are avoided as they will often be the one blamed for any negative consequences, Wallace warns. 

Its why leaders should always be exceptionally clear.

If after communicating with a worker (online or in person) youre still unsure whether you left room for your message to be misconstrued, then Evans insists on turning to your worker for feedback.

She suggests putting your hands up and admitting: I dont think I explained that all too well, how did you interpret that?