The global capital markets are operating at an unprecedented level of intensity. According to the World Federation of Exchanges H1 2024 Market Highlights report, global cash equity has recently witnessed the highest number of trades in a half-year period over the last five years. With trading volumes now routinely eclipsing the dramatic spikes observed during the pandemic, financial technology infrastructure is being pushed to its absolute limits. To survive and thrive in this high-pressure environment, capital market operators must fundamentally rethink how they test, validate, and secure the highly complex software systems that keep global finance moving.
The Determinism Dilemma in Modern Trading Systems
By design, the complex systems underpinning financial markets are distributed and often operate at the microsecond time scale. This makes them inherently non-deterministic. A system’s behavior is constantly influenced by highly volatile external factors, such as the availability and accuracy of market data feeds, massive data volumes, and intricate workflows. Today, this is further compounded by unpredictable sentiment metrics flowing from the interconnected world of social networks.
The « Oracle » Problem
In such a chaotic environment, defining the correct and acceptable behavior for a system—historically known as the « oracle » problem—is incredibly difficult. Traditional testing methods simply cannot keep up with the vast number of data streams, instruments, parameters, and participants interacting simultaneously.
« By design, complex systems underpinning financial markets are distributed and often operate at the microsecond time scale. Any such system is inherently non-deterministic. »
6 Core Principles for Capital Markets Software Testing
To outpace rising workloads, prepare for the unexpected, and meet tightening regulatory requirements, financial institutions should adopt these six tried-and-tested principles:
- End-to-End (E2E) Testing and Comprehensive Modeling: Building precise system models to understand how data moves across the entire transactional lifecycle.
- Specialized Domain Expertise: Relying on QA professionals who possess a deep, native understanding of the financial sector.
- Objective, Independent Analysis: Ensuring the timely delivery of unbiased, data-backed insights obtained through independent studies of the system.
- Sustainable Data Practices: Recognizing the strategic value of test data and managing it as a long-term asset.
- Pervasive, Data-Driven Automation: Moving away from manual processes toward automation strategies built on clean, structured data.
- Functional and Non-Functional Convergence: Testing where system functionality meets real-world performance constraints.
Leveraging AI and Data-First Automation
To build comprehensive and cost-effective test scenarios, the software testing industry is increasingly turning to artificial intelligence. Generative AI can assist quality assurance teams in generating and optimizing complex test libraries. However, analyzing the results of these tests requires even more sophisticated methods, involving both discriminative models and symbolic execution.
Crucially, machine learning-enabled approaches are only applicable to a data-first process. Any testing activities that still rely on heavy, un-digitized human interaction must be optimized and digitized before AI methods can be effectively applied.
« Disparate, narrow-purpose, and out-of-the-box testing solutions only get you so far. Exploring the intersection of functionality and performance is a step that cannot be underestimated when testing complex, non-deterministic solutions. »
Strategic Business Value Beyond Compliance
A sophisticated digital testing strategy does far more than just satisfy supervisory and regulatory bodies. When executed correctly, it introduces systemic optimizations that translate directly to the bottom line:
- Improved Operational Resilience: Achieved through more extensive, versatile, and realistic test coverage.
- Transparent Test Data Management: Utilizing a unified data warehouse for on-demand parametric analyses and supervisory compliance reporting.
- Fast-Paced Adaptability: The agility to pivot quickly in response to shifting regulatory compliance mandates or user-driven changes.
- Accelerated Time-to-Market: Reduced start-of-testing timeframes driven by widespread, pervasive automation.
- Lower Operational Maintenance Costs: Finding and resolving critical defects early in the software development lifecycle to save valuable engineering resources.
Driving Innovation with Exactpro
As an independent provider of AI-enabled software testing services, Exactpro works with exchanges, post-trade platform operators, and investment banks across 20 countries. Leveraging its advanced th2 framework, Exactpro uses artificial intelligence to harness the power of exploratory data analysis and big data analytics. This empowers testers to generate innovative test ideas and accurately interpret test results for matching engines, market surveillance, clearing and settlement systems, and payments APIs. Through its ISTQB®-accredited AI Testing training course—recognized as the Most Innovative Professional Development Initiative by A-Team Group—Exactpro continues to equip the global fintech community with the tools needed to build resilient, high-quality financial software.
By Iosif Itkin, CEO and Co-Founder, and Daria Degtiarenko, Senior Marketing Communications Manager, Exactpro