By Medan Gabbey, CRO, Quod Financial
This is the second article in our multi-part series on industry themes, following Why your Legacy OMS is costing you more than you think? published on February 5, 2025.

Change is inevitable—and often difficult. When buy-side or sell-side firms attempt to address legacy technology, they typically uncover outdated, poorly documented systems with outdated integrations. The common approach of “If it ain’t broke, don’t fix it” leads to obsolescence – much like species that, through natural selection, failed to adapt to their changing environment (sorry, Dodo).
The Trap of Analysis Paralysis
One of the biggest obstacles to modernization is analysis paralysis. Extensive audits of existing processes, often involving external consultants, rarely yield practical outcomes. These lengthy and expensive reviews frequently delay action rather than facilitate meaningful change.
While consultants can play a role, relying on them to analyze internal systems can sometimes be an excuse to defer responsibility, rather than creating a roadmap for success. Even worse, framing technology decisions around solving yesterday’s problems only increases the risk of Darwinian peril.
The Real Secret to Success
The key to success is surprisingly simple. Start with a vision for the future—not an autopsy of the past. Just as buying a new car doesn’t require a detailed analysis of your old one, transforming trading technology starts with a vision for the future.
Ask yourself: What should your trading technology enable? What capabilities should it provide? How should systems and people interact with it? Defining this future state will help you select a vendor that aligns with your goals. This allows you to focus on building the new system while selectively migrating valuable components from the old one.
A successful transition doesn’t require an in-depth understanding of legacy technology—it requires a roadmap for the future. Importantly, your target system should support a staged migration, enabling incremental adoption without disrupting operations.
The Power of Modular Applications
The ideal solution is modular. Instead of attempting a massive overhaul, implement the smallest viable project within your existing infrastructure. The system should provide clear value for every specific workflow, allowing legacy features to be addressed or retired gradually.
This approach can be tailored to individual functional areas—such as algorithms, Smart Order Routing (SOR), middle-office workflows, and market-making—or to specific trading desks and flows, including portfolio trading, convertible bonds, and DMA.
When evaluating modular solutions, firms should consider: Can the system integrate with existing static data? Are real-time APIs available for every function? Does it align with a broader technology strategy? (See Article 3: Leveraging Microservices in Modern Trading, coming on March 5th.) More importantly, does it future-proof workflows, reduce complexity, and allow firms to decommission redundant systems? Finally, is the vendor offering a truly unified technology, or merely a collection of disconnected solutions under one brand?
The right modular approach ensures that firms can replace multiple existing systems, preventing further complexity.
Buy-vs-Build Dilemma: A Common Pitfall
Building OMS/EMS functionality in-house is a monumental challenge. These systems involve years of building complex functional requirements and require ongoing maintenance to remain competitive. Many firms initially define success as “building exactly what we need today”—but a more realistic question is: Are we prepared to maintain and evolve this system for the next 20 years? Redefining success changes the success-criteria in the Darwinian landscape.
The limited number of vendors in the market reflects the complexity of trading technology. If external companies struggle to thrive in this space, in-house development will likely be even more costly and will risk falling behind industry innovation.
The optimal approach is “Buy-AND-Build.” — leveraging a vendor’s proven API infrastructure while tailoring it to your firm’s needs. This is like customizing an existing car rather than rebuilding the engine.
When selecting a vendor, firms should assess whether their API and data model support a Buy-and-Build approach and examine how the vendor is innovating. The key to long-term adaptability and success lies in outsourcing core technology while maintaining flexibility for customization where it truly matters.
Future-Proofing Your Trading Systems
This article provides a high-level overview. The path from legacy to modern systems involves vision, focus and investment in the future — not the status quo. Firms that select a vendor aligned with their end-state vision and adopt a modular, incremental approach will achieve most cost-effective and sustainable modernization. This approach also presents better opportunities to monetize all the workflows within their firm and deliver better outcomes for their clients.
AI is Now a Need, Not a Want for Meeting SEC Regulatory Reporting Requirements
By Laurent Louvrier, VP of Product, Artificial Intelligence at Confluence
The SEC’s introduction of new regulations over the last few years, including the Shareholder Reporting Rule, has meant increased reporting obligations for firms without a corresponding increase in the time, money, and human capital required to meet these obligations.
Firms are under pressure to have the right data and systems in place, leading to rising costs and resources at a time when many firms are also grappling with an overwhelming volume of unstructured data. To source, structure, arrange and report on this data is a significant undertaking given the amount of data teams need to parse. It’s becoming unfeasible for firms to tackle this using their traditional technologies.
New technologies like Gen AI and LLMs allow the automation of manual tasks like checking, reconciling, translating, and reasoning about information that cannot be easily handled by traditional technologies due to the nature of unstructured data. To use the SEC’s Tailored Shareholder Report requirements as an example, manual reconciliation to meet this requirement on or after July 24 may take a typical manager hundreds of business days to reconcile the reports each time. Firms must do this within a 60-day window, which becomes operationally impossible.
Advanced AI models are now capable of mining and pinpointing discrepancies in language and numeric information between financial reports and the TSR, automatically parsing data from third-party financial reports, and interrogating and reconciling it within seconds leading to a 90% increase in efficiency. This directly addresses the key challenges our clients face with data reconciliation and compliance validation under tight deadlines. These solutions convert complex unstructured data into valuable insights, reducing what would typically take weeks of manual work into a streamlined process that delivers significant efficiency and cost savings. While there’s still human oversight needed in all of this, AI can radically improve the efficiency of information and dissemination in financial reporting and compliance.
So, what’s holding firms back from using AI? Largely it has been resistance to change and the perceived risks regarding data privacy and security. To ease any concerns, I would first say companies should examine their business processes to evaluate where they allocate their time and resources and how AI can transform these areas to gain efficiency. A better understanding of business priorities and processes will help make decisions on where to start to leverage AI. While some organizations may have the capability to develop AI solutions internally, it often makes more sense to partner with established vendors who have the business process and AI expertise that can bring proven immediate business value.
As new regulations with more arduous reporting requirements arise, demanding greater time and resources, it will be especially important for firms to replace tedious manual validation processes and embrace productivity-enhancing technologies or risk being left behind. By leveraging AI, investment compliance operations can streamline SEC reporting processes, enhance compliance, and mitigate regulatory risks more effectively.