Research On Ai-Assisted Financial Fraud And Algorithmic Manipulation Prosecutions
1. Introduction
With the rise of artificial intelligence (AI) and algorithmic trading/decision-making in finance, new forms of misconduct have emerged:
Firms claiming to use AI or machine learning to manage funds, when they do not (“AI-washing”).
Trading firms or individuals using automated algorithms to manipulate markets (e.g., spoofing, layering) via high-frequency or algorithmic systems.
Use of AI/algorithms to facilitate fraud: e.g., mis-representations about algorithmic models, misuse of trading bots, or manipulation of algorithmic decision systems.
These give rise to legal challenges: proving intent when algorithms are involved; attribution when algorithms act; regulatory frameworks not yet fully adapted to algorithmic/AI misuse; transparency and explainability of AI; classification of algorithmic manipulation as fraud/market abuse versus legitimate algorithmic trading; and regulatory oversight of AI claims.
2. Key enforcement cases & decisions
Below are more than five significant cases or enforcement actions, with detailed explanation.
Case 1: Michael Coscia (“spoofing” high-frequency algorithmic trading) – USA
Facts: Coscia, through his firm Panther Energy Trading LLC, used automated trading algorithms in various futures contracts (gold, soybeans, euro FX, etc.) from approximately August–October 2011. He placed large “quote” orders on one side of the market he did not intend to execute, promptly cancelled them, to create a false impression of supply/demand and then executed “trade orders” on the opposite side. The strategy used computer algorithms to place and cancel orders at very high speeds (milliseconds) to induce other market participants to react. Department of Justice+1
Legal issue: Whether using an algorithm to place and cancel orders with intent to deceive other market participants constitutes illegal “spoofing” and market manipulation under the Commodity Exchange Act (CEA) and Dodd-Frank Act.
Decision/Outcome: In November 2015 he was convicted of 12 counts (6 counts commodities fraud, 6 counts spoofing) by a U.S. federal jury. Department of Justice+1 In July 2016 he was sentenced to three years in prison. Department of Justice
Significance: This is the first federal prosecution of spoofing under Dodd‐Frank that dealt explicitly with algorithmic (automated) orders. It illustrates that algorithmic manipulation can be prosecuted as fraud/manipulation. It also signals that regulators view automated trading strategies that deceive others as actionable.
Key take-aways: Intent matters (placing orders with the intent to cancel), automated algorithms do not exempt one from liability, and regulatory oversight of high-frequency/algorithmic trading is active.
Case 2: U.S. Securities and Exchange Commission (SEC) v. Delphia (USA) Inc. & Global Predictions Inc. – “AI-washing” claims – USA
Facts: In March 2024 the SEC announced settled charges against two investment advisers (Delphia and Global Predictions) for making false and misleading statements about their purported use of artificial intelligence and machine learning in providing investment advice. SEC+1
Delphia claimed its algorithmic/ML model used “collective data … to make our artificial intelligence smarter so it can predict which companies and trends are about to make it big.” But the SEC found that the firm had not developed the claimed AI capabilities, nor used client data as claimed. Harvard Law Corporate Governance Forum+1
Global Predictions claimed to be “the first regulated AI financial advisor” etc., but misrepresented its AI use. Cleary Enforcement Watch
Legal issue: Whether false/misleading statements about AI-driven investment processes constitute violations of the Advisers Act (Section 206(2) – fraud by investment advisers), the Marketing Rule, and compliance rule obligations.
Decision/Outcome: Both firms consented to cease-and-desist orders and paid a combined civil penalty of US $400,000 (Delphia US$225k; Global Predictions US$175k). They did not admit or deny the findings. ABA Banking Journal
Significance: The first named enforcement actions targeting firms for misrepresenting AI usage in investment advice (“AI-washing”). Signals regulatory focus on truthful claims about AI and algorithmic capabilities.
Key take-aways: Firms must ensure their claims about AI are accurate; regulators are closely scrutinizing marketing and compliance around AI in finance; algorithmic promise can trigger liability if used to induce investor reliance.
Case 3: Algorithmic Manipulation – “Spoofing” and Layering beyond Coscia
Facts: Beyond Coscia, many high-frequency traders and firms have been penalised/indicted for algorithmic strategies designed to mislead markets (spoofing/layering). For example, the CFTC fined a high-frequency trading firm (Tower Research) USD 67.4 million in a deferred prosecution agreement for spoofing from March 2012-Dec 2013. Wikipedia
Legal issue: Use of automated trading algorithms to create artificial order books, disturb market equilibrium, mislead other participants – constitutes market manipulation/abuse under CEA.
Decision/Outcome: Firms and individuals have been fined, barred, or prosecuted. The Coscia case (above) is landmark.
Significance: Algorithmic trading can cross into manipulation/abuse when it is designed to create false signals or mislead other market participants. Algorithms accelerate volume and speed of attacks.
Key take-aways: Regulators are applying existing law (manipulation, fraud) to algorithm-driven trading; algorithmic manipulation is within reach of enforcement; algorithm designers/programmers may also face scrutiny.
Case 4: Algorithmic/AI-Assisted Fraud in Fintech (Crypto-/AI-fraud)
Facts: As noted by Law360 and other sources, enforcement agencies have charged individuals in crypto and fintech sectors for schemes that falsely promised AI or algorithmic trading benefits. For example:
Brian Sewell was charged by the SEC for a scheme promoting a crypto trading course and hedge fund that claimed to use “cutting-edge AI and machine learning” but did not execute those strategies. ArentFox Schiff
David Saffron and Vincent Mazzotta were charged by the DOJ for inducing individuals to invest in trading programs by falsely promising the use of automated AI to trade crypto markets and deliver high returns. Law360
Legal issue: Use of false/misleading statements about algorithms/AI in promotion of investment schemes; fraud by misrepresentation; misappropriation of client funds; deceptive practices.
Decision/Outcome: These matters are still unfolding, but represent a new frontier of AI/algorithm-based fraud enforcement.
Significance: Extends the concept of algorithmic/AI fraud beyond trading markets into investment marketing and fintech. Demonstrates that misuse of “algorithm/AI” claims can be treated as fraud.
Key take-aways: Promises of algorithmic or AI trading need substantiation. Regulators will hold firms and individuals accountable for mis-representing algorithmic capabilities. Algorithmic tools may facilitate fraud if combined with mis-representation.
Case 5: Manipulative Trading by Bank (Swap Dealer) – HSBC Bank USA, N.A. (Example of algorithmic/automated trading manipulation)
Facts: The CFTC issued an order against HSBC Bank USA, N.A., a swap dealer, for manipulative and deceptive trading related to swaps with bond issuers, spoofing, supervision failures, mobile device recordkeeping failures, etc. (Though this case is not purely labelled “AI” it involves automated trading/manipulation) Reddit
Legal issue: Use of automated or algorithm-driven trading to manipulate swaps markets; failure of supervision/controls.
Decision/Outcome: HSBC agreed to pay a civil monetary penalty (USD 45 million) and cease-and-desist from further violations (via CFTC order) though details are via Reddit sourced summary.
Significance: Shows major financial institutions are subject to enforcement for algorithmic trading manipulation, supervision deficiencies, algorithmic control failures.
Key take-aways: Algorithmic manipulation is not confined to small/hedge firms; large banks also face liability when algorithms/trading systems are misused or insufficiently supervised.
Case 6: (Emerging) AI-enabled Money Laundering / Algorithmic Fraud – Research-Based
Facts: Academic research reports increasing use of AI/ML by fraudsters and money-launderers (e.g., synthetic identity fraud, AI-enabled layering, algorithmic manipulation of financial networks) though specific prosecutorial examples are fewer. E.g., a paper on “Digital veils of deception: AI-enabled money laundering and the rise of white-collar cyber fraud” identifies how AI may be weaponized to manipulate financial flows. lawjournals.org
Legal issue: Use of AI/algorithms to commit financial crimes—money laundering, synthetic identity, algorithmic extraction/fraud; difficulties in current frameworks to clearly define and prosecute.
Decision/Outcome: Less developed in terms of public prosecutions; important for future regulation.
Significance: Points to next wave of algorithmic/AI-assisted fraud beyond trading: fraudsters using AI to evade controls, synthesize identities, manipulate transaction networks.
Key take-aways: Legal frameworks must adapt to algorithmic/AI-driven fraud; regulators will need to consider algorithmic liability, explainability, cross-system manipulation.
3. Legal & regulatory challenges in prosecuting AI/algorithmic financial fraud
From the above cases and related commentary, the key challenges include:
Attribution & Intent: With algorithms and automated systems, proving that someone intended fraud (rather than a malfunction or legitimate algorithm) is harder. For example, in spoofing cases, intent to cancel large orders before execution is key.
Algorithmic opacity (“black-box”): AI/ML models and automated trading systems can be complex, difficult to interpret, making it harder to trace which part of the algorithm caused misconduct, and whether a human or algorithm is responsible.
Marketing/representation of algorithmic capability: As the SEC “AI-washing” case shows, firms may claim algorithmic/AI capabilities they do not have; distinguishing between legitimate claims and misrepresentation is legally actionable but requires investigation.
Speed & scale: Algorithms operate at high speed (milliseconds), making detection and evidence gathering more difficult. Regulators must handle vast volumes of data.
Cross-jurisdictional issues / global markets: Algorithmic trading spans markets globally; supervision and enforcement across borders is more complex.
Regulatory lag: Many regulations were drafted before widespread AI/algorithmic use; adapting them (e.g., for algorithmic fraud, synthetic identity fraud via AI) is ongoing.
Supervision of algorithm developers / system operators: When a trading algorithm is misused, is the trader liable? The algorithm developer? The platform? Legal frameworks are still evolving.
Explainability & fairness: Use of AI in finance for detection (AML/CTF) and trading raises concerns about fairness, bias, interpretability—though these are more about compliance than direct fraud prosecution, they impact legal risk. (See article on “Legal implications of automated suspicious transaction monitoring”). SpringerLink
Evidence-preservation & auditability: With algorithmic systems, ensuring logs, algorithm versions, model training data and decisions are preserved and can be audited is a challenge.
4. Conclusion
AI and algorithmic systems are revolutionising financial services—and equally, they are enabling new forms of financial fraud and manipulation. The cases above show that regulators are adapting: from trading-algorithm manipulation (spoofing) to misrepresentation of AI in investment services (“AI-washing”), to early signals of AI-enabled fraud in fintech.
Key take-aways:
Algorithmic trading/manipulation is within the ambit of existing law (fraud, market manipulation) — e.g., Coscia.
Misleading claims about algorithmic/AI capabilities are actionable — e.g., Delphia/Global Predictions.
Supervisory regimes must grapple with algorithmic opacity, high speed, large volume, cross-border issues.
Firms must ensure that any claims about AI/algorithms are backed up; trading algorithms must be supervised; usage must be lawful and transparent.
Legal frameworks are evolving—but enforcement is already happening.
RELATED Blog
0 comments