Case Studies On Criminal Liability For Autonomous Financial Trading Errors
1. Michael Coscia (U.S., 2016) – High-Frequency Trading Spoofing
Facts:
Coscia used an automated algorithm to place large commodity futures orders he intended to cancel quickly (“spoofing”). The algorithm executed trades in gold, soybean meal, and other futures on the Chicago Mercantile Exchange, creating misleading market signals.
Legal Issues:
Whether automated algorithmic trading constitutes intentional spoofing under the Dodd-Frank Act.
If human operators are liable for algorithmic actions.
Outcome:
Convicted of six counts of commodities fraud and six counts of spoofing. Sentenced to 3 years in prison.
Significance:
Establishes that the human behind an algorithm remains criminally liable for manipulative trading.
Confirms that automated execution does not absolve intent.
2. Navinder Singh Sarao (U.S./UK, 2016) – Flash Crash Manipulation
Facts:
Sarao used an automated algorithm to execute “layering” (placing and canceling large orders to manipulate prices) on the S&P 500 E-mini futures market from the UK. His trades contributed to the 2010 Flash Crash.
Legal Issues:
Can a remote operator using automated systems be liable under U.S. law?
Whether spoofing and wire fraud apply to algorithmic trades.
Outcome:
Pleaded guilty to spoofing and wire fraud; forfeited $12.9 million in profits.
Significance:
Reinforced cross-border accountability.
Human intent is key, even when automation is central to execution.
3. Gregg Smith et al. (U.S., 2025) – Automated Spoofing Patterns
Facts:
Traders used algorithmic techniques to spoof precious metals markets, placing and canceling orders to manipulate prices.
Legal Issues:
Whether algorithms executing spoofing patterns satisfy the intent requirement for fraud.
Outcome:
The Seventh Circuit upheld convictions for spoofing and wire fraud.
Significance:
Confirms that algorithmic trading mimicking spoofing is criminal.
Highlights courts’ willingness to infer intent from algorithmic behavior.
4. Citigroup “Fat-Finger” Error (UK/Europe, 2022)
Facts:
A Citigroup trader mis-entered orders in an automated system, creating billions in unintended trades. The algorithm executed orders without checks.
Legal Issues:
Liability for automated trading errors without fraudulent intent.
Regulatory oversight of algorithmic controls.
Outcome:
Fined £27.77m and £33.88m by UK regulators for internal control failures.
Significance:
Even non-fraudulent automated errors carry serious regulatory consequences.
Firms are responsible for adequate safeguards against algorithmic malfunctions.
5. Samarth Agrawal (USA, 2013) – Theft of Algorithmic Trading Code
Facts:
Agrawal stole proprietary high-frequency trading code and transported it to a hedge fund.
Legal Issues:
Theft of algorithms as criminal misappropriation under the Economic Espionage Act.
Applicability of intellectual property law to automated trading systems.
Outcome:
Convicted under the EEA and National Stolen Property Act.
Significance:
Trading algorithms themselves are criminally protected assets.
Theft or misuse of automated trading systems triggers criminal liability.
6. Knight Capital Group Error (USA, 2012) – Algorithm Malfunction
Facts:
An automated trading algorithm deployed by Knight Capital malfunctioned, executing rapid erroneous trades and causing a $440 million loss in 45 minutes.
Legal Issues:
Responsibility for damages caused by autonomous system errors.
Whether negligence in algorithm design or deployment can trigger liability.
Outcome:
No criminal convictions; major financial and regulatory consequences. Firm underwent management overhaul.
Significance:
Shows systemic risk of autonomous trading errors.
Foreshadows potential future criminal/regulatory scrutiny for negligent algorithm deployment.
7. Tower Research Capital (USA, 2012) – Automated Market Manipulation
Facts:
Traders used an automated program to manipulate futures prices by rapidly placing and canceling orders.
Legal Issues:
Automated market manipulation and intent under the Commodity Exchange Act.
Outcome:
Settled with CFTC for $67 million. While not criminal, it demonstrates enforcement against algorithmic market manipulation.
Significance:
Regulators treat algorithmic manipulation seriously, even absent direct criminal prosecution.
Provides precedent for liability for automated trading strategies.
Key Takeaways Across Cases
Human accountability is central: Automation does not remove criminal liability.
Intent can be inferred from algorithms: Courts interpret patterns of algorithmic trading as evidence of intent.
Errors can trigger regulatory liability: Even unintentional malfunctions can lead to heavy fines.
Algorithmic code is protected property: Theft or misuse of trading algorithms is criminal.
System design and oversight matter: Firms must implement safeguards to prevent trading errors.
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