Latest news with #algorithmictrading
Forbes
4 days ago
- Business
- Forbes
Who Owns The Algorithm? The Legal Gray Zone In AI Trading
Brian Moynihan, CEO of Bank of America, is spending $4 billion a year on new technology initiatives ... More such as AI. As artificial intelligence continues to reshape financial markets, it brings with it a core legal and strategic dilemma: who owns the algorithm? A recent Bank of England report raised alarm bells over the systemic risks posed by increasingly autonomous AI trading systems. While much of the concern has focused on market volatility, the equally urgent yet less discussed question is who controls, protects, and is accountable for the intellectual property these systems generate. Under current law, AI systems lack legal personhood and cannot own anything, including copyrights. This means that any code, model, or strategy generated entirely by AI may not qualify for copyright protection unless there is clear human authorship involved. The U.S. Copyright Office has reinforced this position, ruling that non-human authorship cannot enjoy statutory protection. As a result, if an AI independently develops a novel trading strategy, the entity deploying that AI could be exposed, without patent protection, copyright coverage, or a clear paper trail. This creates a competitive vulnerability, especially in algorithmic trading, where uniqueness can deliver billions in edge. Firms have to rethink their IP strategy to make sure human review and oversight are part of the development loop. Firms are increasingly turning to a mosaic of legal mechanisms to protect their proprietary algorithms: Patents can protect novel and non-obvious algorithms with demonstrable utility. However, because algorithms are often deemed abstract ideas, patent eligibility is hard to secure and even harder to enforce internationally. Copyrights protect the specific software implementation and expression of an idea, not the algorithm itself. Crucially, human authorship must be demonstrable, meaning that fully AI-generated code is unlikely to be eligible. Trade Secrets are the most common and practical form of protection for algorithmic trading. Firms treat not only their code but also training datasets, model weights, and even failed strategies ('negative knowledge') as trade secrets. However, this protection is fragile and requires strict internal access controls and documentation procedures. Trademarks, while not protecting the algorithm directly, safeguard the brand identity associated with algorithmic trading services, helping firms establish market dominance and trust. For financial institutions, the strategic implications are clear. If your algorithm is your edge, and the edge isn't legally defendable, you've built a business on shaky ground. The rise of generative AI complicates these protections further. As Dan Bosman, CIO at TD Securities, explained in a company podcast, 'You're not just protecting source code anymore. You're protecting the logic, the training data, the biases, some of which are inherited from external datasets you may not even fully control.' In a McKinsey report, financial executives cited intellectual property uncertainty as one of the primary reasons they have not scaled generative AI pilots beyond internal sandboxes. The fear isn't just regulatory, it's losing control of a core business differentiator. In parallel with these challenges, regulation is catching up. The EU's AI Act, adopted in May 2024, introduces a tiered approach to risk classification for AI systems. High-risk systems, including those used in trading, must include clear human oversight, transparent decision-making, and documented model lineage. While this doesn't solve the ownership question outright, it does pressure firms to ensure there is a traceable human role in the development and operation of trading algorithms. On the U.S. side, policymakers are more fragmented. The SEC, CFTC, and FTC all have overlapping interests in how AI affects financial products, consumer protection, and market fairness. But none have yet addressed IP protection for AI-generated investment strategies head-on. In the absence of a clear legal framework, here's what financial institutions and fintechs should be doing right now: 1. Build Human-in-the-Loop (HITL) Processes: Ensure that even if an AI system generates a new strategy or model, a human analyst or engineer reviews, modifies, or approves it. This not only improves quality control but can establish a stronger case for human authorship. 2. Audit and Document Model Development: Create a transparent pipeline showing how AI-generated content was developed, what data was used, and what decisions were made by humans. This 'model provenance' will be essential for IP protection and regulatory compliance alike. 3. Layer Your Legal Protections: Don't rely on a single form of protection. Use trade secrets to lock down internal know-how, copyrights for implementation, and patents where feasible. Also, enforce NDAs and internal security policies to preserve the defensibility of those protections. 4. Engage Legal Counsel Early: Too many firms involve IP counsel only after a product is market-ready. In the AI era, your legal strategy must be part of your R&D process, especially when AI outputs blur the lines of authorship and originality. 5. Monitor Regulatory Signals: Watch for updates from the U.S. Copyright Office, EU Parliament, and financial regulators. AI policy is moving fast, and what isn't protected today could be covered tomorrow, or vice versa. As the financial sector races ahead with AI, the law is lagging behind. The institutions that succeed in this environment will not be those with the smartest algorithms, but those with the foresight to protect, document, and defend what their systems create. In a world where the next billion-dollar trading edge might be written by a machine, the real competitive advantage lies in knowing who truly owns the outcome and making sure you can prove it. For more like this on Forbes, check out The Legacy Banks Quietly Building The Future Of Finance and The 3 Innovation Challenges Keeping Bank CEOs Awake At Night.
Finextra
14-05-2025
- Business
- Finextra
Horizon Trading Solutions launches smart order router
Horizon Trading Solutions, a global leader in electronic trading solutions and algorithmic technology for capital markets, is launching a transformative Smart Order Router (SOR) - Horizon Compass - to enable institutional and retail brokers to achieve optimal execution and high performance in a rapidly evolving market landscape. 0 This content is provided by an external author without editing by Finextra. It expresses the views and opinions of the author. With legacy SOR solutions filling the market, Horizon's new SOR is purpose-built to address current market dynamics, allowing large and small institutional and retail brokers to efficiently navigate liquidity fragmentation and high trading volumes with no downtime. Horizon Compass ensures accurate and timely pricing in over 80 trading venues Horizon is connected into. Integration of the SOR also allows for effortless compliance with global best execution regulations, such as MiFID II and Reg NMS, facilitated by real-time visibility, traceability, and performance monitoring. With increasing participation in alternative venue types, the SOR interacts not only with lit markets, but with dark pools, conditional orders and more. Horizon's SOR is built on Aeron, a low-latency, high throughput messaging platform. This foundation establishes a resilient platform that can scale and adapt efficiently to high volumes. As market volatility shows no signs of slowing, this offering allows brokers to source liquidity from their algos to manage the substantial growth in trading volumes. The integration of the SOR and Horizon's existing algorithmic trading framework, which includes access to off-the-shelf algorithms and integration of ones developed in-house, is transformative for brokers. It establishes a tech architecture that is flexible and efficient, without the need to independently build a bespoke solution or use a third party SOR. It also enables these firms to benefit from full control over the execution process and meet exact client requirements. This flexibility and improved execution quality will allow regional brokers to increase their market share and compete against the offerings of larger players. Yannick Martin, Head of Agency Trading Product, Horizon Trading Solutions, said, 'Horizon Compass is a ready-to-deploy Smart Order Router that gives you the performance of a custom-built solution, without the complexity, cost, or delays of in-house development. Unlike in-house SORs that take years to mature, Horizon Compass is proven, and latency-optimized. It's built to meet your execution goals today and flexible enough to evolve with your future needs.' Sylvain Thieullent, CEO, Horizon Trading Solutions, added, 'Our innovative new SOR, Horizon Compass, is a crucial milestone in the progression of our agency trading offering. Horizon's global client base of institutional and retail brokers can now benefit from optimal execution, premier liquidity provision, and streamlined regulatory compliance all within one platform.'
