Why capital markets platforms must go modular to harness AI benefits

Insights

• Commercial off-the-shelf (COTS) solutions have been the backbone of trading operations, handling workflows from trade execution to compliance.
• These systems face issues like high costs, long implementation cycles, and limited customization, making them less adaptable to new technologies like AI.
• AI can enhance predictive analytics, anomaly detection, and automation, but traditional COTS platforms struggle to integrate these capabilities.
• Shifting to modular, API-driven architectures allows for greater flexibility and integration of AI, enabling institutions to respond quickly to market changes. These systems reduce vendor lock-in, expedite innovation, and allow institutions to integrate the best solutions for their needs.
• The future of COTS platforms is hybrid, combining foundational capabilities with in-house AI-driven innovations to balance stability and flexibility.

COTS: The foundation of trading operations

Capital markets have depended on commercial off-the-shelf (COTS) solutions for decades. These systems handle front-to-back workflows, including trade execution, settlement, risk management, and compliance across multiple asset classes. They’ve allowed institutions to shorten deployment times and focus their resources on what's core to their business. The adoption rate demonstrates their importance: 83% of institutions are prioritizing investments in data management technology, with a robust ecosystem of hundreds of specialized vendors serving trading, compliance, and risk management functions across the industry.

Leading platforms include Murex, Calypso, Charles River Development (CRD), Summit, and SimCorp Dimension. These platforms have established themselves through comprehensive functional coverage, robust regulatory compliance, and strong vendor support. For example, Murex has a strong presence in cross-asset risk and trading, while SimCorp is widely used in investment management with more than $35 trillion in assets managed on its platforms. Institutions want solutions that work out of the box and are proven to scale as regulations and market demands evolve.

The challenge: Rigid systems in a dynamic market

It isn’t all smooth sailing. The high total cost of ownership, long implementation cycles, limited customization, and dependence on vendors are forcing institutions to rethink — 61% of banks prefer building their own technology stack rather than purchasing COTS products.

But the biggest impact has come from technologies such as artificial intelligence (AI) and cloud. Customers and markets demand more agile, interoperable, and real-time insights. However, traditional COTS products, built as monolithic packages, lack the flexibility to quickly adapt to new technologies or evolving business needs.

AI empowers enhanced predictive analytics, anomaly detection, and intelligent automation capabilities that conventional COTS platforms were not engineered to provide. The inflexibility of conventional COTS platforms creates blockades in critical areas such as trade surveillance, regulatory compliance, risk management, and client onboarding, where AI is already known to deliver benefits. These known benefits include:

• Trade surveillance and compliance: AI models detect insider trading or market manipulation patterns more efficiently than traditional rule-based engines. One study found that an AI surveillance model achieved a 92.4% accuracy in detecting insider trading cases, outperforming the 73.5% accuracy in traditional rule-based surveillance.
• Risk management: Machine learning algorithms improve value-at-risk models by adapting to nonlinear market behaviors.
• Client onboarding and KYC: Natural language processing and generative AI tools accelerate document processing, entity resolution, and risk scoring during onboarding and know your customer (KYC) checks.

Innovation in COTS products often lags behind specialized fintechs or internal data science teams, creating competitive pressure — a trait COTS vendors recognize.

Moving to a composable architecture

COTS vendors are responding by shifting toward modular, composable architectures. This entails breaking down monolithic COTS systems into smaller interoperable services via application programming interfaces (APIs), cloud-native tools, and microservices. In this paradigm, COTS platforms are no longer the "system of everything," but one of many plug-and-play components. This transformation enables institutions to integrate AI features more effectively, improving automation while allowing rapid responses to regulatory changes and market conditions.

AI flourishes in this model. Institutions can enhance their existing COTS platforms by integrating specialized AI services tailored to specific functionalities such as pricing optimization, sentiment analysis, and operational efficiency.

• Trade validation automation: AI automates the application of thousands of pre- and post-trade validation rules across investment mandates, reducing manual errors and saving significant time.
• Credit market liquidity: CRD’s order and execution management system (OEMS) integrates with LTX, an AI-driven trading platform provider, to assess liquidity and connect buy-side clients with natural counterparties using AI algorithms. This increases price transparency enabling the consistent best execution of trades. In another instance, Murex is enhancing its platform by adopting cloud-native microservices and exploring AI-driven risk analytics.
• Portfolio management: SimCorp, a multi-asset investment management solutions provider, partnered with IntelliBonds, a fintech, to offer cloud-based connectivity, offering portfolio construction and cost optimization for fixed income portfolios and bond scoring engines that identify performance risks months in advance. The company has also made investments in predictive analytics with the objective of optimizing portfolio management.
• Custom integration: Quant teams can integrate machine learning models into Murex pricing workflows via APIs, while NLP engines extract data from unstructured documents for SimCorp's accounting modules.

This modular approach mitigates vendor lock-in, expedites the time to market for innovation, and enables institutions to integrate the best solutions for their specific requirements from a range of suppliers.

Strategic recommendations: Building for the future

COTS vendors are already working to embed AI directly into their core functions and shifting to open, API-based architectures to enable flexibility. Most banks are already incorporating AI in their daily processes, and improved COTS flexibility will enable seamless integration with the bank systems and COTS products. Steps underway include the following:

1. AI-native functionality: Integrate AI capabilities directly into core processes rather than providing mere "AI connectors." This includes AI-driven dashboards, intelligent workflow optimization, and real-time predictive analytics.
2. Open ecosystems: Transition to API-driven, cloud-native platforms that integrate seamlessly with AI and fintech ecosystems. The global API management market is projected to grow from $6.85 billion in 2025 to $32.48 billion by 2032, driven largely by demand for seamless integration, scalability, and innovation. As businesses embrace composable architectures, closed architectures are no longer sustainable or effective for meeting evolving business and technological demands.
3. Data as a service (DaaS): Data is the lifeblood of AI. Provide clean, real-time, normalized data as a foundation for AI applications. Data quality and accessibility are critical competitive advantages in AI-driven environments.
4. AI governance and explainability: As regulatory scrutiny of AI intensifies, financial institutions increasingly require vendors to deliver robust model governance, bias identification, and explainable AI capabilities. These features not only help institutions comply with requirements such as the EU AI Act, but also help build trust among Tier 1 institutions, regulators, and customers. Recent research indicates that global regulatory focus remains on explainability and bias, and that 86% of financial institutions anticipate expanded explainable AI and governance controls in the coming years. Solutions offering transparent decision-making, auditability, and regular model risk assessments are becoming essential for maintaining institutional trust and regulatory compliance.

The future is hybrid

COTS platforms will continue to play a crucial role in capital markets, particularly in ensuring regulatory compliance, operational resilience and process standardization. Nevertheless, their future relevance will be contingent upon their ability to effectively integrate with AI-driven ecosystems and facilitate composable, client-centric innovation.

But the traditional buy vs. build debate is evolving into hybrid models. Institutions are using COTS for foundational capabilities while developing AI-driven differentiators in-house. Cloud infrastructure and open-source AI tools have reduced barriers to entry, enabling this flexible approach.

Success will belong to vendors that adapt to client ecosystems and foster collaborative innovation, rather than simply offering the broadest functional footprint. As the capital markets technology sector continues to show strong performance, the most successful platforms will be those that balance stability with flexibility, enabling institutions to maintain compliance and operational resilience while supporting rapid innovation in an AI-driven landscape.