How does AI enhance financial market abuse detection and risk prevention, and what adaptable strategies can the space industry adopt from these?

The Northumbria University Law School is home to an expert Space Law Team researching on current legal issues and challenges in Earth’s orbits, on celestial bodies, and across tech such as governance challenges regarding cybersecurity or the use of AI with satellite systems. The Law School also hosts various space law and regulating tech modules at the undergraduate and master’s levels. The Law School includes two Space Law LLM Programmes: Space Law LLM (full-time) and Space Law LLM (part-time, distance learning). To learn more about space law at the Northumbria University Law School please visit: https://www.northumbria.ac.uk/business-services/research-and-consultancy/space/space-law-and-policy/

Author: Maria Giannaka, Space Law LL.M. Student, Northumbria University, Newcastle-upon-Tyne

Key words: Artificial Intelligence (AI); Financial market abuse detection; Risk prevention; Space industry; Machine Learning; Market Abuse Regulation (MAR); Behavioural customer profiling; Real-time anomaly detection; Cybersecurity; Satellite data monitoring; Mission risk assessment; Fraud prevention strategies; Orbital risk-prevention automated systems.

Artificial Intelligence (AI), a technology of growing prominence that reshapes our world, is rapidly becoming more and more integrated into our daily lives. As a branch of computer science focused on creating intelligent machines that mimic human intelligence , essentially AI empowers computers to learn, performing tasks and solve problems with unprecedented speed and efficiency through brain-inspired neural networks. This widespread adoption has spurred technology companies across diverse sectors to invest heavily in AI. In the financial industry, for example, a key application is market abuse detection, where AI's ability to analyse client transactions, perform risk-scoring, and segment customers into risk profiles is crucial for developing strong fraud prevention strategies.

This article argues that the regulatory and technological framework for AI-driven financial market abuse detection offers a robust, directly transferable model for the space industry to address its own legal and ethical challenges, particularly regarding space debris and orbital sustainability.

How financial services leverage AI for market abuse detection?

Under the European Union’s main Market Abuse Regulation (MAR), unlawful behaviour in financial transactions that undermines financial market integrity is prohibited. The regulation contains provisions to prevent and detect insider dealing, unlawful disclosure of inside information, and market manipulation; in essence, market abuse is a form of illicit financial fraud that gives certain parties an unfair advance.

Commission Delegated Regulation (EU) 2026/957 specifies the technical systems and procedures firms must use to prevent, detect and report these abusive practices. Although recitals are not legally binding, Recital (1) in this Regulation explicitly supports using automated systems (a form of AI), stating that “The whole process is likely to require some level of automation.”

To fulfil these legal requirements, many financial institutions, including banks and exchange groups, use advanced surveillance software from specialised fintech companies like “Scila”. These systems are practical examples of the AI-driven automation mentioned in the regulation. They monitor trading activity in real-time and automatically generate “alerts” when they detect suspicious patterns that match predefined rules for potential market abuse. Compliance teams then monitor and investigate these alerts to determine if the activity warrants reporting to regulatory authorities. This reporting is vital, as authorities study these incidents to prevent future risks and use the findings to enhance the AI-driven parameters and thresholds, making the AI surveillance models more robust.

A key method behind these AI surveillance models is the creation of detailed behavioural customer profiles. Machine learning algorithms analyse extensive historical market data – every order, modification, and trade – to understand the typical patterns of a specific market participant. Each new market action is then assessed against these dynamic profiles and assigned a real-time risk score based on parameters like order size, timing relative to news, order-to-trade ratios, and patterns indicative of “layering” or “spoofing”.

When a potential anomaly is detected, AI triggers an alert for more in-depth investigation by compliance teams. This is where AI tools are crucial for boosting efficiency; instead of manually sifting through thousands of transactions, compliance teams are presented with a focused list of high-risk cases/transactions. Since market abuse investigations are incredibly time-intensive, this allows teams to prioritise their efforts effectively. As the methods of financial fraud evolve, leveraging this technology gives institutions a crucial, proactive defence against both current and emerging threats to market integrity.

So how can the space industry adapt AI policies to include strategies and surveillance models used in financial market abuse detection and risk prevention?

The space industry, whilst seemingly disparate from finance, faces similar challenges in terms of data analysis, anomaly detection, and risk management. The principles of AI-driven financial market surveillance offer a powerful and directly applicable risk framework for ensuring the long-term sustainability and legal compliance of space operations; and for a successful AI implementation, the space industry, in collaboration with the financial industry, needs access to extensive, high-quality data, robust technological systems, and a strong ethical framework.

How can lessons learned from AI-driven financial market abuse prevention be applied to the challenges of the modern space industry?

My career in financial services compliance and audit has given me the opportunity to witness the transformative power of AI in safeguarding global markets. Through the support of this advanced technology, the industry learned to deploy sophisticated AI in its daily operations to detect and mitigate illicit activities that pose a systemic risk to the stability of the global economy. Today, the space industry seems to face a parallel challenge. Here, the critical threats are not unauthorised trades but the tangible dangers of orbital collisions and the escalating crisis of space debris. This is not merely a technical analogy as the space industry shall also fulfil its legal obligations under international and domestic law. For example, Article VI of the Outer Space Treaty (1967) holds states responsible for their national activities in outer space. Therefore, the same AI-driven principles used to find a single anomaly amidst the (now) zettabytes of world data[vi] could be repurposed to design and predict catastrophic orbital events.

The following points explore how lessons from financial surveillance can provide a robust framework for a comprehensive space system to ensure the long-term sustainability of space operations:

Space missions - risk assessment - Financial institutions use AI to assess the risk of fraudulent transactions. Similarly, AI can be used to assess the risks associated with space missions. AI can analyse data from previous missions, orbital simulations, space weather and environmental factors to identify potential hazards, optimise mission planning and predict the likelihood of debris collisions or system failures. This directly supports a state’s legal responsibility to prevent harmful contamination of space and avoid causing adverse impacts and changes in the Earth’s environment.

Space systems - cybersecurity risk - Financial institutions are constantly battling cyberattacks. Space systems are also vulnerable to cyber threats which could violate a state’s responsibility for the actions of its non-governmental activities. AI-powered cybersecurity systems can be used to detect and prevent intrusions into satellite networks and ground control systems. AI can analyse network traffic patterns to identify suspicious activity and block malicious attacks before they compromise a mission.

Satellite/spacecraft monitoring - risk detections (real-time anomaly detection) - Just as AI identifies unusual financial transactions, it can be used to detect anomalies in satellite/spacecraft data or telemetry. AI can analyse vast streams of sensor data to identify deviations from normal operating parameters, potentially indicating malfunctions or impending failures. These early detection of these anomalies can prevent costly mission failures and, more critically, mitigate the creation of new space debris, which is a key concern of the UN Committee on the Peaceful Uses of Outer Space (COPUOS) guidelines on space debris mitigation.

Satellite/spacecraft - risk prevention and maintenance - AI is used to predict when financial systems may be compromised and to trigger autonomous actions when fraud is detected. Similarly, AI can also be used to predict when satellite/spacecraft components are likely to fail, enabling proactive maintenance. This can reduce downtime and extend the lifespan of assets or make decisions in real-time, adapting to changing conditions and responding to unexpected events without human intervention, which is essential for managing the growing congestion in Earth’s orbit.

In conclusion, the AI-driven models revolutionising financial market abuse detection offer a powerful tool and a directly applicable framework for the space sector. The principles of AI revolutionise financial fraud detection, offering unparalleled capabilities in analysing vast data-sets, identifying anomalies in real-time, and predicting potential risks or threats. This technology also empowers financial institutions to proactively combat fraud, enhance security and market stability.

The lessons learned from these applications are not confined to the financial sector; they offer valuable insights for industries facing similar challenges. The space industry can leverage AI to enhance spacecraft monitoring, mission risk assessment, cybersecurity, or orbital risk prevention to fulfil its growing legal and ethical responsibilities under international law.

However, successful AI implementation hinges on the same pillars as in finance: access to high-quality data, reliable technological infrastructure, and robust ethical considerations. As both financial fraud and the complexities of space exploration continue to evolve, AI will undoubtedly play an increasingly vital role in safeguarding critical assets.

The core legal question for the coming decades is this: How can international bodies like the UN COPUOS create a regulatory mandate that encourages the adoption of AI to safeguard the space environment, much like MAR has done for financial markets?