2025 OCIE Exam Priorities Overview
The SEC's Division of Examinations (formerly OCIE - Office of Compliance Inspections and Examinations) publishes annual examination priorities that signal where regulatory scrutiny will be most intense. For algorithmic trading platforms, investment advisers using automated systems, and quantitative trading firms, understanding these priorities is essential for examination readiness.
In 2025, the SEC has made clear that algorithmic trading, AI-driven investment tools, and automated portfolio management systems are top-tier examination priorities. This isn't a passing interest - the SEC has dedicated specialized teams to understanding and examining these technologies.
Why Algorithmic Trading Is a Priority
The SEC's focus on algorithmic trading stems from three core concerns: (1) the potential for systemic risk from automated systems, (2) the difficulty of traditional compliance oversight on "black box" algorithms, and (3) investor protection concerns when advice comes from machines rather than humans. These concerns mean heightened scrutiny and more detailed document requests.
Key 2025 Focus Areas for Algo Platforms
Algorithm Governance
- Development and testing protocols
- Change management procedures
- Model validation practices
- Risk limit monitoring
- Kill switch and error handling
AI/ML Disclosures
- Marketing accuracy vs. reality
- Form ADV completeness
- Model assumptions disclosure
- Performance attribution clarity
- Limitations and risks explained
Fiduciary Compliance
- Suitability of algo recommendations
- Conflicts embedded in models
- Best execution for auto trades
- Client interest vs. firm revenue
- Ongoing monitoring adequacy
Algorithmic Trading Focus Areas
Based on the 2025 exam priorities, recent risk alerts, and enforcement actions, the SEC will concentrate on these specific areas when examining algorithmic trading platforms.
1. Algorithm Design and Development
Examiners want to understand how my algorithms were designed, who designed them, and what objectives they're optimized to achieve.
| Focus Area | What Examiners Look For | Common Issues |
|---|---|---|
| Design Documentation | Written specifications, logic diagrams, decision trees | Verbal agreements with no documentation |
| Optimization Target | What the algorithm is designed to maximize or minimize | Optimizing for firm revenue not client returns |
| Training Data | Data sets used to train ML models, time periods covered | Survivorship bias, regime dependency |
| Development Team | Qualifications of developers, oversight structure | Developers with no investment expertise |
| Version Control | Change tracking, rollback capability, audit trails | No record of algorithm changes over time |
2. Model Risk Management
The SEC expects firms to have robust model risk management frameworks that identify, measure, and mitigate risks inherent in algorithmic systems.
- Model Inventory: Complete catalog of all algorithms in production
- Risk Classification: Tiering models by potential impact on clients
- Independent Validation: Third-party or independent internal review of high-risk models
- Performance Monitoring: Ongoing comparison of model predictions vs. actual outcomes
- Escalation Procedures: Clear protocols when models behave unexpectedly
OCC SR 11-7 as Guidance
While the OCC's SR 11-7 "Supervisory Guidance on Model Risk Management" applies to banks, the SEC has referenced similar principles for investment advisers. The framework - validation, ongoing monitoring, and governance - provides a useful template for algorithmic trading compliance.
3. Pre-Trade Risk Controls
Automated trading systems must have controls that prevent erroneous or unauthorized trades before they execute.
| Control Type | Purpose | Examination Focus |
|---|---|---|
| Order Size Limits | Prevent inadvertent large orders | Are limits appropriate for strategy and market? |
| Price Collars | Block trades at prices far from market | How are collar bands determined and updated? |
| Duplicate Order Prevention | Avoid sending same order multiple times | Testing of duplicate detection logic |
| Message Throttling | Limit order message rates | Compliance with exchange requirements |
| Kill Switch | Immediately halt all automated trading | Testing frequency and documented procedures |
Document Requests for Algo Platforms
When the SEC examines an algorithmic trading platform, the document request list (DRL) will be significantly more detailed than for traditional advisers. Understanding what to expect helps me prepare in advance.
Sample Document Request List - Algorithmic Trading Examination
I. Algorithm Documentation
- Complete algorithm specifications, including logic diagrams and pseudocode
- Source code for all algorithms used in production (with annotation)
- Algorithm development documentation, including design rationale
- Training data sets for machine learning models, with data dictionaries
- Version history showing all algorithm changes since inception
- Model validation reports (internal and third-party)
- Performance attribution analysis by algorithm
II. Testing and Backtesting
- Pre-deployment backtesting results for all algorithms
- Walk-forward test results and out-of-sample validation
- Stress test scenarios and results
- Sensitivity analysis documentation (parameter variations)
- Paper trading results before live deployment
- Documentation of testing methodologies and assumptions
- Comparison of backtested vs. live performance
III. Governance and Oversight
- Algorithm governance committee charter and membership
- Meeting minutes for algorithm oversight committee
- Policies and procedures for algorithm development and changes
- Approval documentation for new algorithms or material changes
- Personnel qualifications (developers, validators, compliance)
- Training records for staff involved in algorithm oversight
- Organizational charts showing reporting lines
IV. Risk Controls and Monitoring
- Pre-trade risk control documentation and parameter settings
- Kill switch procedures and testing records
- Real-time monitoring dashboards and alert configurations
- Error logs and incident reports for algorithm malfunctions
- Post-trade surveillance reports
- Breach reports for risk limit violations
- Disaster recovery and business continuity plans for trading systems
V. Client-Facing Materials
- Form ADV Part 2A (all versions during exam period)
- Marketing materials referencing algorithms or AI
- Client communications explaining algorithm changes
- Performance reports showing algorithm-generated returns
- Risk disclosures specific to algorithmic strategies
- Client questionnaires used to determine suitability
- Advisory agreements describing algorithm-based services
VI. Trading Records
- Order generation logs (algorithm-produced orders)
- Execution records with algorithm attribution
- Best execution analysis for algorithm-driven trades
- Trade allocation documentation
- Broker selection rationale for automated execution
- Soft dollar arrangements affecting algorithm execution
- Payment for order flow disclosures and analysis
VII. Compliance Program
- Annual compliance reviews addressing algorithmic trading
- Code of ethics and personal trading policies
- Conflicts of interest assessments (algorithm-specific)
- Cybersecurity policies and incident response plans
- Books and records policies for algorithm documentation
- Vendor due diligence if using third-party algorithms
Source Code Requests
The SEC can and does request actual source code. While I can discuss proprietary concerns, outright refusal to produce code is not an option. I should be prepared to provide annotated code with explanations, potentially under confidentiality protections.
Algorithm Governance & Testing Review
The SEC expects algorithm governance to mirror the rigor applied to traditional investment processes - with additional controls given the speed and scale of automated systems.
Governance Framework Components
| Component | Key Elements | Examination Questions |
|---|---|---|
| Committee Structure | Cross-functional team with authority over algorithms | Who approves new algorithms? What are their qualifications? |
| Development Standards | Coding standards, documentation requirements, peer review | Show me your development standards document. |
| Testing Requirements | Mandatory testing before production deployment | What testing must occur before an algorithm goes live? |
| Change Management | Approval process for modifications to existing algorithms | How do you determine if a change is "material"? |
| Periodic Review | Scheduled reassessment of algorithm appropriateness | How often do you review each algorithm's performance? |
| Decommissioning | Process for retiring underperforming algorithms | When was the last time you retired an algorithm and why? |
Testing Expectations
The SEC will scrutinize not just whether I tested, but how thoroughly and realistically.
- In-Sample vs. Out-of-Sample: Algorithms must be tested on data not used in development
- Multiple Market Regimes: Testing should cover bull markets, bear markets, high volatility, low volatility
- Stress Scenarios: How does the algorithm perform in tail events (2008 crisis, flash crash, etc.)?
- Parameter Sensitivity: Testing variations in key parameters to understand robustness
- Transaction Cost Realism: Backtests that ignore slippage and market impact are unrealistic
- Look-Ahead Bias: Ensuring the algorithm doesn't use future information in historical tests
Common Testing Deficiencies
- Backtests run only on the same data used to train the model (data mining)
- Unrealistic assumptions about execution (using closing prices, ignoring liquidity)
- No stress testing or only testing in favorable market conditions
- Cherry-picking test periods to show favorable results
- No documentation of testing methodology or parameters used
- Failure to update tests when market conditions change
Backtesting Documentation Requirements
Backtesting is where many algorithmic platforms stumble during examinations. The SEC views backtesting documentation as critical evidence of due diligence before deploying strategies on client assets.
What Must Be Documented
- Data Sources and Quality: Where did the historical data come from? Corporate actions adjusted? Survivorship bias addressed?
- Testing Period: Specific start and end dates, rationale for period selection
- Transaction Cost Assumptions: Commissions, spreads, market impact, slippage assumptions
- Rebalancing Frequency: How often the strategy trades in the backtest
- Constraints Applied: Position limits, sector limits, liquidity constraints
- Results: Full performance metrics including returns, volatility, drawdowns, Sharpe ratio
- Multiple Scenarios: Results under different market regimes and parameter settings
- Comparison to Benchmark: How the strategy performed vs. relevant benchmarks
Backtesting Best Practices
| Practice | Why It Matters | How the SEC Evaluates |
|---|---|---|
| Walk-Forward Analysis | Prevents overfitting by testing on unseen data periods | Are walk-forward results materially worse than in-sample? |
| Monte Carlo Simulation | Assesses sensitivity to randomness and sequence of returns | What's the distribution of potential outcomes? |
| Realistic Execution | Ensures backtest reflects actual trading conditions | Compare backtest assumptions to live trading costs |
| Documented Assumptions | Transparency about what's built into results | Can you defend every assumption in your backtest? |
| Version Control | Track changes to strategy over time | Show me backtests from when you first deployed vs. now |
The "Curve Fitting" Problem
Examiners are trained to spot curve fitting - algorithms that are over-optimized to historical data and unlikely to perform out-of-sample. Warning signs include excessive parameters, perfect backtests, and lack of economic rationale for strategy behavior. If my backtest looks too good to be true, examiners will assume it is.
Performance Attribution Analysis
The SEC expects me to be able to explain where my algorithm-generated returns come from. Performance attribution demonstrates I understand my own strategies and can identify when they're not working as designed.
Attribution Components
- Factor Decomposition: How much return comes from market beta, size, value, momentum, etc.?
- Alpha vs. Beta: Separating skill-based returns from market exposure
- Sector/Asset Class Contribution: Which sectors or assets drove performance?
- Trade-Level Analysis: Profitability by trade type, holding period, market condition
- Costs Impact: Attribution of returns net of all trading costs
- Benchmark Comparison: Excess returns vs. stated benchmark
Why Attribution Matters for Examinations
| Exam Question | What Attribution Shows |
|---|---|
| "Is your algorithm delivering the returns you promised?" | Comparison of expected vs. actual performance sources |
| "Are you taking risks you disclosed to clients?" | Risk factor exposures vs. Form ADV risk descriptions |
| "How do you know when your algorithm stops working?" | Changes in attribution pattern signal strategy degradation |
| "Are your marketing claims accurate?" | Attribution validates or contradicts marketing narratives |
Proactive Attribution Review
Performing regular (monthly or quarterly) performance attribution and documenting findings demonstrates strong governance. If I can show examiners that I monitor attribution and adjust strategies when patterns change, this evidences good fiduciary practice.
Marketing & Advertising Review
The SEC has brought numerous enforcement actions against firms making misleading claims about algorithmic or AI capabilities. Marketing review is a critical exam focus area.
High-Risk Marketing Claims
Claims That Trigger SEC Scrutiny
- "AI-Powered Investment Platform": Must demonstrate actual AI/ML, not just rules-based algorithms
- "Outperform the Market": Requires substantiation and proper disclaimers
- "Eliminate Human Emotion/Bias": Algorithms embed developer biases - this claim is often false
- "Sophisticated Quantitative Models": Must match actual complexity of models used
- "Proven Track Record": Performance must be calculated according to GIPS or with clear methodology
- "Tax Optimization": Must actually implement tax-aware strategies, not just harvest losses
Marketing Documentation Required
- Substantiation Files: Support for every material claim in marketing
- Performance Calculations: Workpapers showing how advertised returns were calculated
- Approval Records: Who reviewed and approved marketing materials before use
- Distribution Records: Where marketing materials were used (web, social media, email)
- Comparison to Form ADV: Marketing claims must align with disclosures
SEC Marketing Rule (Rule 206(4)-1) Application
| Requirement | Algorithm-Specific Consideration |
|---|---|
| General Prohibitions | Cannot make untrue statements about algorithm capabilities or past performance |
| Performance Advertising | Must present gross and net returns; backtests must be labeled as hypothetical |
| Third-Party Ratings | If using algorithm ratings/rankings, must disclose methodology and conflicts |
| Testimonials/Endorsements | Client testimonials about algorithm performance require disclosures and oversight |
| Predecessor Performance | Can't claim current algorithm performance from prior similar strategies without meeting requirements |
Social Media and Algorithmic Claims
Tweets, LinkedIn posts, and YouTube videos are advertising under SEC rules. If I or my team make claims about algorithm performance or capabilities on social media, those claims must be substantiated and compliant with advertising rules. Many enforcement actions arise from social media posts.
Cybersecurity & Data Protection Exam
Algorithmic trading platforms are high-value cybersecurity targets. The SEC's cybersecurity examination program focuses on both protecting client data and ensuring trading systems can't be compromised.
SEC Cybersecurity Examination Focus
| Category | Examination Questions |
|---|---|
| Governance | Who is responsible for cybersecurity? How often does board/management review cyber risk? |
| Risk Assessment | When did you last assess cyber risks? How do you identify critical systems? |
| Access Controls | How do you control access to trading algorithms and client data? |
| Data Protection | Is sensitive data encrypted at rest and in transit? How do you protect PII? |
| Incident Response | Do you have a written incident response plan? When was it last tested? |
| Vendor Management | How do you assess cybersecurity of third-party service providers? |
| Training | What cybersecurity training do employees receive? How often? |
Algorithm-Specific Cyber Considerations
- Code Access Controls: Who can view, modify, or deploy algorithm code?
- API Security: If algorithms access market data or execute via APIs, how are API keys protected?
- Model Theft Protection: Proprietary algorithms are valuable IP - what protects against theft?
- Data Integrity: How do I ensure market data fed to algorithms isn't corrupted or manipulated?
- Kill Switch Security: Are emergency shutoff controls protected from unauthorized use?
Regulation S-P and Safeguards Rule
The SEC's Regulation S-P requires written policies to protect customer information. The recent Safeguards Rule amendments (effective June 2023) require specific controls including encryption, access controls, incident response plans, and annual risk assessments. These aren't optional - they're regulatory requirements.
Fiduciary Duty for AI Models
When my algorithm provides personalized investment advice, I owe clients a fiduciary duty - even though the advice comes from code, not humans. The SEC has made clear this duty is non-waivable and non-delegable.
How Fiduciary Duty Applies to Algorithms
Duty of Care
- Adequate Basis: Algorithm must gather sufficient client information to make suitable recommendations
- Reasonable Investigation: I must understand how the algorithm works and whether it's appropriate
- Ongoing Monitoring: Duty continues after recommendation - I must monitor client accounts
- Best Execution: If algorithm executes trades, must seek best execution
Duty of Loyalty
- Client First: Algorithm must prioritize client interests over firm revenue
- Conflict Disclosure: All conflicts embedded in algorithm design must be disclosed
- No Self-Dealing: Algorithm can't favor proprietary products without disclosure and consent
- Informed Consent: Clients must understand conflicts before agreeing to algo advice
Examination Questions on Fiduciary Compliance
- "What client information does your algorithm collect?" - Testing adequacy of intake questionnaire
- "How does the algorithm determine suitability?" - Understanding recommendation logic
- "What conflicts exist in your algorithm design?" - Identifying undisclosed conflicts
- "How do you monitor ongoing suitability?" - Assessing continuous duty compliance
- "Can clients opt out of algorithmic recommendations?" - Testing informed consent
- "What happens when the algorithm recommends unsuitable trades?" - Override and escalation procedures
The Questionnaire Problem
Many robo-advisers fail examinations due to inadequate intake questionnaires. If my questionnaire doesn't gather sufficient information about client financial situation, goals, risk tolerance, and experience, the algorithm cannot possibly provide suitable advice. Examiners will test whether my questionnaire meets fiduciary standards.
Conflicts Embedded in Algorithms
| Conflict Type | Example | Disclosure/Mitigation Required |
|---|---|---|
| Revenue Optimization | Algorithm designed to maximize platform fees | Eliminate or obtain informed consent with full disclosure |
| Proprietary Products | Algorithm favors firm's own ETFs or funds | Disclose conflict; demonstrate suitability independent of conflict |
| Payment for Order Flow | Execution venue selection influenced by PFOF | Disclose arrangement; demonstrate best execution |
| Data Provider Incentives | Compensation from data vendors whose info influences recommendations | Disclose relationship and potential bias |
| Training Data Bias | Model trained on data that favors certain strategies or products | Disclose limitations and scenarios where model may underperform |
Preparing for Your First Exam
If my algorithmic trading platform has never been examined, preparation is critical. First exams often result in more deficiency letters because firms haven't yet learned what the SEC expects.
Pre-Exam Readiness Checklist
- Document Inventory: Create comprehensive list of all algorithm-related documentation I have (and gaps)
- Mock Exam: Have counsel or consultants perform mock examination using sample DRL
- Form ADV Review: Ensure Form ADV accurately describes algorithm capabilities and limitations
- Marketing Audit: Review all marketing materials for unsupported claims
- Testing Documentation: Compile all backtesting, stress testing, validation reports
- Governance Review: Document governance structure and approval processes
- Code Documentation: Prepare annotated source code with explanations for non-technical reviewers
- Performance Substantiation: Prepare performance calculations with full methodology
- Conflict Assessment: Identify and document all potential conflicts
- Cybersecurity Documentation: Compile cyber policies, risk assessments, incident response plans
Typical First Examination Timeline
Receive exam letter with initial document request list; engage securities counsel immediately
Gather requested documents; review with counsel; produce to SEC via secure portal
Initial call or meeting with examiners to discuss scope and logistics
Examiners review documents, interview personnel, test algorithms and systems
Additional document requests based on initial review; clarifying questions
Examiners present preliminary findings; opportunity to respond or clarify
Receive written findings; typically 30 days to respond with remediation plan
Submit remediation plan; implement changes; possible follow-up with examiners
The 90-Day Head Start
Don't wait for an examination letter. I should prepare as if an exam could arrive tomorrow. Maintaining exam-ready documentation continuously is far easier than scrambling when the letter arrives. Most successful firms perform annual mock exams to identify gaps proactively.
Team Preparation
| Role | Preparation Focus |
|---|---|
| CCO (Chief Compliance Officer) | Primary point of contact; understand entire compliance program and algorithm governance |
| CTO/Lead Developer | Be prepared to explain algorithm design, testing, and technical architecture |
| Portfolio Managers | Understand investment rationale, performance attribution, and strategy evolution |
| Legal Counsel | Review all document productions; present for sensitive interviews; advise on exam strategy |
| Operations Staff | Understand trade execution process, best execution analysis, broker selection |
Interview Protocol Training
Before examiners arrive, I train all personnel who might be interviewed on proper protocol: answer only what's asked, don't speculate, say "I don't know" rather than guess, take notes on questions, and inform CCO of all examiner interactions. Poor interview performance can turn a routine exam into an enforcement referral.
Common First-Exam Deficiencies
Top Deficiencies at First Algo Platform Exams
- Inadequate Documentation: No written algorithm specifications, testing records incomplete
- Governance Gaps: No formal governance committee or approval process for algorithms
- Form ADV Inaccuracies: Vague or outdated description of algorithmic services
- Marketing Overstatements: Claims about AI/ML capabilities not matched by reality
- Insufficient Testing: Backtests that don't cover multiple market regimes or stress scenarios
- Conflict Disclosure Failures: Undisclosed conflicts in algorithm design or execution
- Cybersecurity Deficiencies: No written cybersecurity policies or incident response plan
- Best Execution Gaps: No systematic analysis of execution quality for automated trades
- Questionnaire Inadequacy: Intake forms don't gather sufficient info for suitable advice
- Books and Records: Incomplete records of algorithm changes, client communications
Remediation Strategies
If I receive a deficiency letter, how I respond determines whether the matter closes or escalates to enforcement.
| Deficiency Type | Effective Remediation | What Not To Do |
|---|---|---|
| Documentation Gaps | Immediately create documentation going forward; reconstruct past docs where possible | Create backdated documents - this is obstruction |
| Policy Deficiencies | Adopt written policies; train staff; demonstrate implementation | Adopt policies but not actually follow them |
| Testing Inadequacy | Perform comprehensive testing now; adopt testing standards for future | Argue that testing isn't necessary for my type of algorithm |
| Disclosure Failures | Amend Form ADV immediately; communicate changes to clients | Delay amendments or provide minimal disclosure |
| Conflicts Not Addressed | Eliminate conflict, fully disclose, or obtain informed client consent | Argue the conflict is immaterial without client perspective |
The Remediation Response
My deficiency response should have three components: (1) acknowledgment of the finding, (2) explanation of what I've already done to address it, and (3) commitment to specific future actions with timeline. Attach evidence of remediation already completed. This demonstrates I take compliance seriously.