An Introduction to Imandra Markets
First published on Tabb Forum.
Can AI and digital twins be used to solve problems with operational resiliency and unlock innovation in financial markets?
Global financial markets are under pressure to unlock new revenue streams through innovation and, as highlighted in a recent IOSCO consultation on market outages, improve operational stability. Active innovation trends include ESG, digital assets, new data products, and the use of AI. However, challenges with system complexity, automation and organisational inefficiencies can drag innovation and profitability.
Is there a way that financial markets can leverage innovative ideas from other industries to accelerate their growth? For example, a recent McKinsey article highlighted that “digital twins are revolutionising how decisions are made within factories, and forward-thinking manufacturers are getting ahead of the technology curve to drive efficiency”.
We discuss how the digital twin approach combined with AI can unlock innovation and increase operational resiliency within financial markets.
Industry challenges with operational resilience
As seen over recent years, major exchange outages have a market-wide impact. Industry participants still ask, “Why does this still happen?”. Regulators and market participants demand a greater level of operational resiliency.
Despite industrywide initiatives to improve financial market operational resilience, including attempts to agree on alternative closing price procedures, many root causes persist. The problem is that there is no silver bullet solution.
A recent IOSCO survey shared in their “Consultation Report Market Outages” published in December 2023 shows that software-related issues caused most market outages. Examples include failed software releases and invalid instructions. Reading the paper highlights the breadth of causes in the chart below, which indicates the scale of the challenge that exchanges face.
Innovation headwinds
With operational resiliency in mind, one can imagine the challenges in delivering swift innovation. New products and features must be designed, specified, built and tested by different internal groups and communicated clearly to external stakeholders such as trading firms and regulators. The coordination required to align all interested parties is enormous, and the industry-wide expectation is to get it right the first time.
Consider the following examples of exchange upgrades:
- A new order type to accommodate a new asset class.
- A system transformation project to replace a legacy platform.
- A new fee type based on complex order interactions.
- A matching logic change to introduce an anti-gaming mechanism.
All market participants feel the impact of these upgrades, many of whom have to alter their trading systems to accommodate the change. The exchange has to change its public and private documentation, adjacent systems (post-trade, surveillance, regulatory reporting, etc.), coordinate data providers, have trading firms conformance test, the list goes on. In essence, the entire ecosystem has to understand and implement the change precisely.
It prompts the following questions for the exchange:
- How much due diligence is required?
- Is the design correct?
- How can we predict the impact throughout the entire platform?
- How can we ensure the correct understanding across all stakeholders?
- How is this tested, and have we identified defects before launch?
From the project’s inception to final deployment and customer adoption, one perceived conflict is present: What is the trade-off between risk management and cost?
There are also non-functional exchange upgrades, which are mandatory for numerous reasons, including information security, hardware replacements, firmware upgrades, and system performance. They each pose comparable challenges for the exchange.
Roots of the problem
Traditional product and software development practices can’t keep pace with the algorithm complexity we see in modern trading and exchange systems.
Knowledge
The knowledge and understanding of the exchange system are distributed across multiple teams and specialists. Documentation such as business requirements, rulebooks, and technical user guides are written manually using prose, tables, and worked examples, often under-specified and quickly outdated, leading to the following challenges with the system build:
- There is no way of ensuring that new requirements are logically consistent across the ecosystem.
- Developers hope the business requirements have been accurately understood and captured.
- Test programs lack formal measures against which they can assess their success.
- Accountable stakeholders lack transparency, which exposes them to unbounded risks.
In other words, there is no formal synchronisation between stakeholder understanding & documentation and the exchange system.
Identification of defects
We know from Boehm’s Law and real-world experience that fixing bugs and glitches in production is exponentially more expensive than in the design phase. Yet, there is a lack of scientific techniques used within financial markets to identify defects and design flaws. Given the complexity of modern financial systems, is it possible to verify the impact of the new or upgraded system design before it is even built? And if not, identify defects earlier in the development lifecycle.
An interesting analogy is when a new bridge is built. The architects and civil and structural engineers communicate over an exact blueprint that describes the details of the bridge. Structural engineers apply laws of physics to the blueprint to see if it is structurally sound, even before the ground is broken. This up-front analysis at the design stage saves time, money and lives. This model-based design verification approach is commonplace in safety-critical industries and microprocessor design.
The alternative solution — a digital twin
What is a digital twin?
IBM defines a digital twin as “a virtual representation of an object or system that spans its lifecycle, is updated from real-time data, and uses simulation, machine learning and reasoning to help decision making”.
Digital twins for financial markets
With so many stakeholders, the degree of complexity and the demand for rapid innovation, financial systems should transition from age-old, prose-based specifications to a precise design. Representing the financial system’s business logic in a digital executable model that one can interrogate allows exchanges to:
- Verify properties about the exchange system behaviour
- Generate test cases to ensure the correctness of the exchange system
- Run an audit against production trading data
- Generate English-precise, prose documentation
And in a more advanced state, machine learning and generative AI can be harnessed to:
- Create new revenue streams with innovative data products
- Integrate large language models for ease of access
Imandra is leading the way with this technology in financial markets. The following section describes how the “Imandra Markets” digital twin combined with Automated Reasoning tackles the abovementioned challenges by replacing age-old analogue processes with a digital approach.
Imandra Markets — a digital twin powered by Automated Reasoning
The Imandra Markets Digital Twin
Imandra’s patented technology and scientific advances of automated logical reasoning empower the creation of a fully functional and logically precise “digital twin” of any complex financial software system.
The Imandra Markets digital twin is positioned at the heart of the software and product development lifecycle. The golden source specification aligns and deepens stakeholders’ understanding digitally. It is dynamically connected to the test and production exchange environments through data synchronisation to ensure compliance and correct implementation. Imandra Markets offers unique “what-if” scenario analysis and new feature innovations, paving the way for predictive data products for trading firms’ systematic strategy calibration.
Trading venues benefit from the next generation of design verification, rigorous testing, resiliency, business intelligence and growth opportunities.
Solving the knowledge gap
Instead of relying on English-prose documents and long email chains, the exchange stakeholders can digitise the business logic design of the venue, allowing them to have one central and precise blueprint, just like the architects and structural engineers do when designing and building a new bridge.
The Imandra Markets digital twin is more than just code. It is a living digital specification that systematically aligns all stakeholders to one golden source of exact truth. No time is wasted in decision-making and figuring out how the exchange system works. Knowledge is preserved across the entire team, and no information is lost.
A core tenet of the product is to ensure that all stakeholders can use it, not just those who can read the code. Imandra Markets has tools catering to all users and their specific areas of interest. Here are two examples:
- A compliance officer can directly tie the precise prose description of a system feature and usage statistics to the underlying system via the digital twin. Imandra Markets can generate comprehensive governance reports, making regulatory compliance and approvals simple and quick.
- A product manager and business analyst can take a cut of the model and ‘play’ by simulating the impact of new designs. Imandra Markets will systematically assess the impact of new designs, which are displayed through powerful side-by-side orderbook replay tools. This “what-if” scenario analysis and impact assessment accelerates the creative thought process and unlocks innovation.
Identifying defects and improving operational resilience
Financial market systems are so complex humans struggle to understand the entire system’s behaviour, especially when relying on analogue specifications. There are simply too many edge cases to consider, keep track of and test. Breakthroughs in AI and mathematics allow us to model exchange rules and regulations precisely and apply rigorous logical AI to automate regulatory analysis and testing, all while providing logical audit trails.
Based on the digital design of the exchange or venue, Imandra Markets uses highly automated logical reasoning AI to ensure its compliance and correct implementation. There are two critical pillars to this:
- System Verification — A Verified Design. By using AI-powered logical reasoning to verify system behaviour, Imandra Markets will identify exchange design defects upfront before the software development process begins, which isn’t possible in the analogue world of prose-based requirements and specifications.
- Systematic Validation — Automated Test Plan. Imandra can systematically analyse the digital twin to identify all possible behaviours and edge cases. This analysis, “symbolic reasoning”, is a form of generative AI underpinned by logical reasoning, which yields accurate results.
This leads to high-coverage automated test generation, used to test the exchange system and identify functional and non-functional issues.
The result is that exchanges benefit from a design and system defect identification step-change. The perceived trade-off between risk management and cost is broken.
Imandra Markets unlocks the pace of software development, gives financial institutions confidence in their operational resilience, and proves that their system design will behave as intended.
Business intelligence
Trading firms are well versed in using order, trade and market data to help inform order routing decision-making, for example, TCA. They aim to understand better the intersection between (i) the exchange features and (ii) the statistical liquidity profile to calibrate their strategy to meet their trading objectives.
Imandra Markets gives the exchange operators a unique tool to analyse their customers’ interaction with the trading system by running many ‘what-if’ scenarios to create an actionable intelligence data product to help customers optimise the whole potential of the exchange system and its features.
Timing a racing car on a race track offers an interesting analogy. The crucial performance measurement visible to the outside world is the one against the stopwatch, the lap time. However, the car’s potential is only visible to the racing team. The team’s engineers can analyse the driver using internal telemetry to see if they fully exploit the car’s capability to make it go faster.
Imandra Markets gives exchanges the tools to help trading firms realise the unused potential of the exchange system.
The takeaway
If there is one takeaway from reading this article, it is that a better way of managing complex financial systems exists.
By taking inspiration from safety-critical industries, companies can move away from traditional analogue processes by using digital twins to manage their systems and reap the benefits.
The Imandra Markets digital twins offer a leap forward in exchange system resiliency, accelerate system development and open up avenues for business intelligence and new revenue streams.
Exchanges have already begun using this approach. Platforms in Europe collectively responsible for one-quarter of the equity market volume actively use this method.
With industry-wide focus and mandates to explore the use of AI, Automated Reasoning is under the spotlight, and when combined with the digital twin approach, it can be game-changing.
About the author
Paul Brennan is the Chief Strategy Officer and Head of Growth for Financial Markets at Imandra, Inc.
Before working at Imandra, Paul spent 15 years at Goldman Sachs in numerous roles, most notably as the Chief Operating Officer of the European trading venue SIGMA X MTF.
Paul has deep expertise in managing regulated businesses based on complex mission-critical technology, including steering them through business and technology transformations.
As an early adopter, Paul became the first customer of Imandra and demonstrated the value of Automated Reasoning AI within Financial Markets.
