The standard risk management tools that many prop firms rely on excel at detecting “rule-based” violations such as HFT and news gap trading.
However, they cannot prove in audits whether those rule applications were “fair.” This article explains the limitations of commoditized risk management and why the “explainable audit trail” provided by AI MQL becomes the last line of defense for CCOs.
1. Introduction: The Blind Spot of Commoditized “Rule Detection” and Prop Firms
The proprietary trading (prop firm) industry is being redefined by technology. As the market matures, acquiring traders and managing risk have become the new standard, executed on sophisticated platforms integrated with CRM (Customer Relationship Management) 1.
1.1. The “Standard Equipment” of Prop Firm Risk Management
Modern technology vendors like Axcera 3 and Kenmore Design 1 offer integrated solutions to streamline firm operations. At the core of these platforms is undoubtedly the “risk management” function.
Kenmore claims that through “real-time risk monitoring,” administrators can oversee trader activities and detect violations 1. Similarly, Axcera emphasizes “real-time risk monitoring” and “automated rule enforcement,” automatically enforcing rules like daily loss limits and maximum leverage 5.
The list of violations targeted by these “standard equipment” systems has become the industry’s common language. Specifically, it includes “News Gap trading,” “HFT (High-Frequency Trading),” “exploitive strategies,” “account hedging,” “passing services,” and “trade duration abuse” 1.
These systems are indeed functional in identifying traders who violate rules, allowing firm administrators to “deactivate violators” 1.
1.2. The Fundamental Questions for CCOs and CTOs beyond “Detection”
However, these standard tools cannot answer the more serious and fundamental questions faced by the management of prop firms, particularly the CCO (Chief Compliance Officer) and CTO (Chief Technology Officer).
These tools excel at detecting the “What”—”who violated the rules.” This is an undeniable fact.
The issue raised in this article is that these tools cannot prove to regulators or auditors the “Why”—”why the rule was applied (or not applied).”
Can the CCO withstand questions like the following during an audit?
“Your system detected an HFT violation by Trader X and suspended his account. However, during the same period, Trader Y, who exhibited almost the same trading pattern, was not suspended. Was this difference in judgment influenced by the fact that Trader Y was a high-revenue trader for your firm? Prove that the application of this rule was not ‘arbitrary’ but based on a consistent and fair policy.”
Can the CTO technically support this CCO’s “proof” by instantly reconciling fragmented logs across multiple systems (CRM, trading platforms, risk engines) to provide an auditable data foundation?
1.3. Purpose of this Article: from Commodity Risk to Competitive Advantage of “Accountability”
Competitor vendors’ marketing materials emphasize the trader-facing function (firm defense) of “defending against violators” 1. However, this is essentially an admission that the firm itself exercises powerful “discretion” in deciding “which violators to block and by what criteria.” Competitor tools provide an interface for exercising “discretion” but offer no function (firm defense) to prove to regulators that this “discretion” was fair.
Furthermore, “AI-driven fraud detection” touted by Axcera exacerbates this issue 5. If a CCO responds, “We punished because AI flagged it,” regulators will ask, “Why did the AI flag it? Is there any bias in the algorithm?” The introduction of unexplainable AI only increases the “black box” for the CCO.
The purpose of this article is to highlight the fatal gap between “Detection” and “Proof.” We demonstrate how “standard risk management” represented by Axcera and Kenmore exposes CCOs to the new regulatory risk of “arbitrariness.” We explain why the “Explainable Audit Trail” provided by AI MQL is the only solution that goes beyond mere violation detection, protecting the CCO’s legal responsibility and providing true technical control to the CTO.
2. The New Regulatory Risk of “Arbitrariness”: Perspectives of the SEC and FINRA
Risk managers at prop firms are constantly tasked with determining whether specific traders have violated rules. However, the regulatory perspective has already shifted beyond that. The issue is not individual “rule violations” themselves but whether the “application of rules” across the firm is consistent, fair, and non-arbitrary.
2.1. SEC’s New Focus: the Danger of “Untailored” Policies
Regulatory oversight has shifted from merely checking the existence of a “compliance manual” to a substantive evaluation of whether the policy is specifically designed and operated to address the company’s unique risk realities 6.
The recent enforcement approach of the U.S. Securities and Exchange Commission (SEC) clearly demonstrates this shift. According to analysis by WilmerHale law firm, the SEC did not charge investment advisers with illegally trading MNPI (Material Nonpublic Information) in recent enforcement actions 7.
Instead, they took action solely because their compliance policies were not “nature of such investment adviser’s business” as required by Section 204A of the Advisers Act of 1940 “sufficiently specific” to consider the 7 7.
Specifically, one adviser considered CLO (Collateralized Loan Obligations) trading a significant part of their business, yet their policy did not specifically address the handling of MNPI related to the loans underlying CLOs 7. Another adviser, who took “core strategies” 7 such as participating in creditor committees, lacked policies to address the risk of inadvertently receiving MNPI under those specific circumstances 7.
This SEC enforcement logic directly relates to risk management at prop firms. Simply implementing off-the-shelf risk management tools like Kenmore or Axcera and applying their standard rules (e.g., HFT detection) does not fulfill the monitoring obligations “tailored” to the “nature of their business”—that is, the highly specialized task of evaluating and managing thousands of challengers and fund managers using diverse strategies, as required by the SEC.
2.2. “Cherry Picking”: a Typical Case of Arbitrary Application and CCO Responsibility
The most prominent example of “arbitrariness” becoming a direct target of enforcement is the SEC’s accusation of “cherry picking” schemes 8.
This is a typical “arbitrary allocation practice” where fund managers assign favorable trades (profitable trades) to themselves or specific preferred accounts and assign unfavorable trades (loss-making trades) to other clients’ accounts 8.
The legally most significant point in this case is that the SEC accused the firm’s CCO (Chief Compliance Officer) personally of “ignoring the various red flags” and “aiding and abetting” the violation 8.
When applying this logic to prop firm operations, the implication is clear. If a CCO overlooks the “red flags” of HFT violations by “high-performing Trader A” (for the firm’s profitability) while immediately punishing (suspending the account of) “low-performing Trader B” for similar violations, there is a clear legal risk of being considered “arbitrary compliance application” with the same structure as “cherry picking.”
2.3. Structural Defects of Rule-Based Systems
The issue of “arbitrariness” is not only a matter of individual CCO ethics or discretion but also stems from the structural defects of the “rule-based” risk management systems provided by Axcera and Kenmore.
Academic research on algorithmic risk assessment (RAI) in the field of criminal justice clearly demonstrates this issue 9. These risk assessment tools are introduced with the expectation of providing objective judgment. However, according to Harvard University researcher Ben Green, these tools not only “fail to provide objectivity” but also create numerous “sites of discretion” 9. The algorithm itself can function as an “opaque, unpredictable” 11 black box based on developer biases and incomplete data.
The systems of Axcera and Kenmore are the “risk assessment tools (RAI)” in prop firms. While they appear to provide objective rules, they actually create countless “sites of discretion” for the CCO by determining “which rules,” “at what thresholds,” and “to which groups of traders” to apply.
The Financial Industry Regulatory Authority (FINRA) itself points out that the mere fact that companies set “unreasonably designed monitoring controls (e.g., thresholds that are too high or too low)” 12 is a regulatory concern. In other words, the CCO bears new risks simply by “setting the rules.” Unless they can objectively answer the regulator’s question, “Why was the HFT detection threshold set at 100 milliseconds? Why not 50 milliseconds?” the rule itself can serve as evidence of “arbitrariness.”
3. The Last Line of Defense for CCOs: The Challenge of Proving “Duty of Care”
As standard risk management tools create “sites of discretion” and regulators increasingly scrutinize “arbitrariness,” prop firm CCOs face unprecedented personal legal liability.
3.1. Increasing Personal Liability of CCOs
According to analysis by regulatory consulting firm InnReg, CCOs face personal legal liability (criminal, civil, reputational risk) if their company fails to comply with relevant laws 13. A survey shows that 74% of CCOs express concern about their personal liability 13.
This risk is not limited to cases where CCOs “actively participated in misconduct” or “made false statements” 13. As InnReg points out, the SEC lacks clear guidance on the scope of CCO liability 13, placing CCOs in a highly uncertain environment where they could be held personally accountable for “any compliance violation” 13.
Particularly serious is when gaps in the compliance program lead to violations, and regulators retrospectively determine that “a competent professional should have identified and filled those gaps” 13. This logic is identical to the case where the CCO was deemed to have “ignored red flags” 8 in the “cherry picking” case.
3.2. The Only Defense: Denying “Wholesale Failure”
In such a harsh environment, one of the few defenses for CCOs to protect themselves is, as experts from Proskauer law firm point out 14, to prove that the violations that occurred were not a manifestation of “wholesale failure“—that is, “a basic and long-term failure equivalent to the program not existing at all”—but rather isolated incidents that occurred despite the CCO’s “good-faith effort” 14.
“Regulators highly value good-faith efforts” 15, and to avoid the risk of being considered “wholesale failure,” CCOs must maintain “records of proactive and functional programs” that demonstrate “compliance is not merely formal” 14.
The regulatory paradigm has shifted. Previously, proving “duty of care” meant documenting activities such as “implementing policies,” “purchasing tools,” and “conducting training.” However, in the current era where “tailoring” 7 and “ignoring red flags” 8 are questioned, proving “duty of care” has shifted to documenting judgments, specifically “why this alert was investigated and why this alert was dismissed.”
3.3. Standard Tools Cannot Prove “Duty of Care”
Returning to the question posed in Section 1, can the system logs of Axcera and Kenmore withstand the proof of “duty of care,” that is, the denial of “arbitrariness”?
The answer is clearly “No.”
Suppose regulators present the trading records of high-revenue Trader Y, who was not punished, and ask, “This may have been below the alert threshold, but wasn’t it a ‘red flag’ 8 as pointed out by the SEC?”
The CCO can only respond by looking at Kenmore’s dashboard 1 and saying, “The system did not issue an alert.” Or they might say, “An alert was issued, but after investigation, it was deemed not problematic.”
However, when regulators then demand, “Show the ‘investigation process’ and the ‘reason for deeming it not problematic’ with records from that time,” the CCO can only remain silent. This is because competitor standard tools lack the function to objectively and immutably record the “investigation process” and the “basis for judgment (Why).” The CCO’s testimony relies solely on the “presumption of innocence” and does not function as an audit trail at all.
The lack of reliable audit trails puts the CCO in a difficult position during audits. Worse, as Proskauer experts warn 14, the lack of reliable audit trails tempts the CCO to create “backdated” documents on the spot, “misleading the SEC staff to hide deficiencies.” This is a far more serious violation than the original violation and could end the CCO’s career. The lack of reliable audit trails leads the CCO to personal ruin.
4. The CTO’s Dilemma: Technical Debt and Audit Trail Discontinuity
The serious legal risk faced by the CCO is not just a compliance department issue. Its root cause lies in the structural defects of the technical infrastructure managed by the CTO (Chief Technology Officer), namely “technical debt” and “audit trail discontinuity.”
4.1. CCO’s Demands vs. CTO’s Reality
When the CCO is questioned about “arbitrariness” by regulators, the CTO is required to instantly provide a complete history of “all alerts that violated specific rules (e.g., HFT)” and “all responses (punishments, overlooks, investigations)” across multiple systems.
However, the reality faced by the CTO is legacy systems 16 and data silos. Many financial institutions still rely on mainframes written in old languages like COBOL 16. These systems lack interoperability and operate in batch processing, making them completely unsuitable for the real-time cross-system audits required by the CCO 16.
Kenmore’s solution 1 itself suggests that data is inherently fragmented, consisting of multiple components such as “CRM,” “dashboard,” “challenge tracking,” and “payout workflow.”
The “technical debt” faced by the CTO not only causes operational inefficiencies (e.g., reduced development speed 18) but also creates “regulatory costs” by technically aiding and abetting 14 the CCO’s “duty of care violation” 8 due to the lack of auditability.
4.2. Fatal Flaws of Traditional Audit Trails
Traditional audit trails are designed not to meet the “proof” requirements demanded by the CCO. This issue is not industry-specific but a challenge recognized by global regulators.
The International Organization of Securities Commissions (IOSCO) severely criticizes the limitations of existing audit trail data in its detailed report on market surveillance challenges posed by technological innovation 19.
- Issue 1: Data Fragmentation: Trading information is dispersed across multiple exchanges and systems 19, making “cross-market surveillance” extremely difficult 19.
- Issue 2: Lack of Compatibility: There are “significant disparities” in the information collected (e.g., customer IDs, orders, trades), and different formats prevent regulators from reconciling the data 19.
- Issue 3: Lack of Context (Why): Traditional logs (even FINRA’s CAT (Consolidated Audit Trail) 21) collect “transaction” data (=What) but find it difficult to instantly link it to the “investor’s identity” and “reason for judgment” behind it 19.
To prove “no arbitrariness,” the CCO must “manually” 5 piece together these fragmented logs, which cannot meet the speed and demands of audits and requires significant time and cost 22.
The greatest irony for the CTO is that implementing “modern” solutions like Axcera and Kenmore 1 does not provide a true solution. These tools themselves are closed “silos.” They need to integrate with “trading platforms (MT4/MT5/cTrader)” 23 and other core systems, but as IOSCO points out 19, they are not designed to provide a truly “unified” audit trail. They are dashboards for “detection,” not ledgers for “proof.”
Implementing these tools now is nothing more than “purchasing future technical debt to solve current problems” for the CTO.
5. AI MQL’s “Explainable Audit Trail”: From Detection to Proof
There is an approach that solves this triple challenge of regulators questioning “arbitrariness” 7, CCOs struggling to prove “duty of care” 14, and CTOs facing “data silos” 19.
While standard tools remain at detecting the “What,” AI MQL’s solution enables proof of the “Why.” This is achieved through the fusion of two core technologies.
5.1. Two Core Technologies Provided by AI MQL
Technology 1: Explainable AI (XAI)
AI MQL’s risk engine fundamentally differs from mere rule-based flagging or the black-box AI provided by competitors 5. Our system is based on “Explainable AI (XAI).”
XAI is the practice of making AI system outputs (decisions) “understandable, interpretable, and explainable by humans” 24. When regulators or auditors 26 ask, “Why was this transaction flagged as suspicious?” “Which factors classified this customer as high-risk?” 25, XAI provides clear “reasons” and “grounds.”
This allows CCOs to prove the “objectivity” and “rationality” of their judgments (or system judgments), ensuring regulator trust 25 and reducing the risk of bias 25.
Technology 2: Immutable Audit Trail
The “Why” generated by XAI, the “Decision” made by the CCO (or risk officer) based on it, and the original “Transaction (What)” are recorded in the “Immutable Audit Trail,” the latest advancement in RegTech 30.
This approach, represented by technologies like blockchain 31, ensures that all records are “tamper-proof” 31 and guarantees “data integrity” 31.
As KPMG’s report indicates, this technology enables “simplified reporting and enhanced regulatory compliance through automated immutable audit trails” 30.
5.2. The Value of “Explainable Audit Trail”
AI MQL combines these two technologies—XAI that generates “reasons” and Immutable Ledger that protects “data”—to create what we call the “Explainable Audit Trail.” The “reasons” generated by XAI are solidified as legal “evidence” by the Immutable Ledger. This chain is AI MQL’s “deep moat” that competitors cannot replicate.
This provides decisive value to both CCOs and CTOs that cannot be obtained with standard tools. The comparison table below clearly illustrates this paradigm shift.
Table 1: Paradigm Shift Comparison of Risk Management Solutions
| Evaluation Criteria | Standard Risk Management (Axcera, Kenmore, etc.) | AI MQL: Explainable Audit Trail |
| Key Features | Rule Detection (HFT, News Gap, etc.) | Proof of Decision (Reasons for Detection and Enforcement) |
| Audit Trail | Fragmented Logs (Recording of “What”) | Unified Immutable Records (Recording of “Why”) |
| Response to Regulators | “Existence Proof” of Compliance | “Proof of Fairness” in Compliance Application |
| CCO’s Legal Risk | High (Cannot dispel suspicion of “arbitrariness”) | Low (Can objectively prove “duty of care”) |
| CTO’s Technical Challenges | Preservation of Technical Debt (Data Siloing) | Elimination of Technical Debt (Auditable Single Source of Truth) |
Value for CCOs:
Completely dispels suspicion of “arbitrariness” in regulatory audits. All decisions (including punishments and overlooks) can be presented as tamper-proof evidence of “when, who, what, and why” decisions were made 34. This serves as the “last line of defense” to protect the CCO’s personal legal responsibility.
Value for CTOs:
Liberates from the nightmare of siloed legacy systems 16 and fragmented, incompatible audit logs pointed out by IOSCO 19. AI MQL provides a “Single Source of Truth” 36 for compliance, dramatically improving auditability and data governance.
While competitor tools treat risk management as a cost center for “finding violators,” AI MQL’s solution elevates risk management to a governance function of “proving the firm’s fairness,” transforming it into a source of competitive advantage through “transparency” to investors and regulators 37 38.
6. Conclusion: Avoid “Commodity Risk” and Implement “Accountability”
This article has demonstrated the paradigm shift in risk management faced by modern prop firms.
6.1. Paradigm Shift in Risk Management
In the prop firm market, the ability to “detect” HFT and news gaps is no longer a differentiator but merely a minimum requirement for survival, a “commodity.”
The real risk is not the traders’ violations themselves but the firm’s response to those violations being deemed “arbitrary” by regulators. Standard tools represented by Axcera and Kenmore can “detect” rule violations but are not designed to prove the “fairness” of their application. Relying on these tools means accepting “commodity risk”—that is, using the same tools as competitors, having the same audit weaknesses, and facing the same legal liabilities.
6.2. Final Recommendations for CCOs and CTOs
To CCOs:
Your “duty of care” 14 cannot be proven with a contract stating you implemented risk management tools. In an era where the SEC pursues “ignoring red flags” 8 and “inadequate policy tailoring” 7, your “good-faith effort” 15 depends on whether you can present the “reasons (Why)” for all your judgments as objective evidence.
To CTOs:
Tools designed solely for “detection” 1 cannot meet the “proof” demands of CCOs, leading to manual data reconciliation during audits and increasing technical debt 17. What is needed now is a next-generation “audit trail” foundation that addresses the challenges highlighted by IOSCO 19, ensuring auditability and data integrity 36.
6.3. AI MQL’s “Moat”
If what Axcera and Kenmore offer is commoditized “Detection,” then what AI MQL provides is unique “Proof.”
Our “Explainable Audit Trail” fuses XAI (Explainable AI) 25 and Immutable Ledger 30, offering the only answer to regulators’ suspicions of “arbitrariness.” This serves as the “deep moat” in prop firm compliance and the last line of defense to protect the CCO’s legal responsibility.
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