In recent years, I have witnessed a shift in how regulators think about oversight. Traditionally, agencies like the FCA and Ofcom have relied on periodic audits, inspections or self-reporting to identify compliance breaches. But as markets digitise and transactions happen in real time, this approach is often too slow and fragmented. Regulators need continuous visibility – in effect becoming “automation-first” – using cloud-native, event-driven systems to ingest and analyse data streams from the market and flag problems proactively.
The FCA’s own Strategy (2025-30) calls to “improve our processes and embrace technology to become more efficient and effective”. Similarly, Ofcom’s strategy emphasizes using AI and large data sets to monitor compliance (for example, publishing unique spectrum usage data for AI development). These UK policies underscore the mandate: public bodies should default to cloud and automation (as per the Government’s Cloud First policy) and invest in technology to act faster on emerging harms. In this article I describe a reference architecture for such an “automation-first” regulator, survey UK initiatives (case studies and policy), and sketch a working example of real-time anomaly detection.
From Reactive Audits to Real-Time Surveillance
I’ve seen how fixed-interval reporting or end-of-year audits often miss misconduct that happens between checks. As regulators note, new forms of market abuse can emerge quickly in digital markets, requiring constant monitoring. For instance, the FCA has observed that trade surveillance must evolve to detect “ever more complex forms of market abuse”. In a proactive model, every relevant event (trade, payment, telecom signal, etc.) is streamed into a data platform as it happens. Rather than waiting for a quarterly report, the regulator continuously correlates these events, applying analytics and machine learning to detect anomalies or patterns of concern. This can drastically reduce the lag between misconduct and detection.
Adopting this approach aligns with UK policy. The FCA’s senior leadership has committed to being a “smarter regulator” by upgrading its technology and systems. The UK’s Cloud First policy also explicitly instructs public bodies to “automate the provisioning and management of as much of their infrastructure as possible, reducing manual processes”. Practically, this means using managed cloud services, serverless compute, and data pipelines wherever feasible. In short, regulatory agencies in the UK are expected to move to modern, automated architectures by default.
A Cloud-Native, Event-Driven Architecture
To implement this vision, I propose a cloud-native streaming architecture (see figure below). Data from regulated firms and markets (e.g. transaction feeds, trade venues, telecommunications signals, sensor networks) flows into a streaming ingestion layer. This could be built on technologies like Amazon Kinesis/Azure Event Hub/Kafka or EventBridge, which handle high-throughput event ingestion. The ingestion layer standardises and persists the raw events (for example, in a multi-tenant Kinesis stream or Kafka topic).
Once in the stream, a processing layer performs real-time analytics. Lightweight compute (e.g. AWS Lambda or Azure Functions) or managed streaming engines (e.g. Amazon Kinesis Data Analytics/Apache Flink/Azure Stream Analytics) consume the data to run validations, transformations, and anomaly-detection algorithms. For instance, each incoming transaction event could be scored by a trained machine-learning model or checked against statistical thresholds to flag unusual patterns. The processing layer can also enrich or aggregate data on the fly. (Notably, AWS documentation outlines how Flink and Lambda can be used to process streaming records for cleansing, enrichment and analytics.)
Processed results are then routed to one or more destination layers. Depending on use case, these include data lakes and warehouses for long-term storage and batch analytics (e.g. Amazon S3 + Redshift/Snowflake) as well as operational stores for immediate alerting (e.g. Amazon OpenSearch/Elasticsearch for dashboards). In practice, we might push summarized events into a time-series database or stream-to-database connector. The key is to ensure the regulator has both: (a) a real-time view for instant monitoring, and (b) a historical archive to support deep analysis. For example, the architecture above illustrates streaming data landing in Redshift for analytics, with alerts pushed to dashboards and end-users.
On top of this pipeline, an alerting and response subsystem is needed. Whenever the processing layer detects a red flag (e.g. an anomalous trade pattern or a sudden spectrum interference), it should trigger notifications and workflows. This could use cloud services like AWS SNS/Azure Event Grid or integrate with messaging apps (Teams, Slack) to notify analysts. AI-driven decision support (such as automated classification of the issue) could feed into case-management tools. The point is regulators would treat alerts as first-class events: each leads to investigation or enforcement action far more quickly than was possible with monthly reports.
Collectively, this design embodies the event-driven paradigm. Each event in the ecosystem triggers downstream processing and potential action. Data flows continuously rather than in batches. This architecture also scales: cloud streams can elasticly handle spikes in volume, and managed analytics services auto-scale with usage. Importantly, we leverage public-cloud managed services (Kinesis, Lambda, etc.) as the Cloud First guidance recommends, avoiding over-customisation. In line with best practice, all infrastructure is defined as code and auto-provisioned, so the system itself self-documents and can adapt to new data sources or algorithms with minimal friction.
Data-Driven Monitoring and Analytics
A crucial element is the analytics applied to the data streams. Statistical and machine-learning models can be implemented in real time. For example, to spot trading anomalies one might train a model on normal market data and then score each new transaction as it arrives. If a pattern of orders diverges from expected behaviour (as measured in milliseconds), the system flags it for review. Even simple techniques can work: for example, a z-score approach marks any transaction value more than 3 standard deviations from the mean. Below is a toy Python illustration of this idea (in practice, production code would be event-driven and far more robust):
import numpy as np
# Simulate a stream of transaction values
transactions = np.random.normal(100, 10, size=1000) # normal transactions
# Inject a few anomalies
transactions[::200] = transactions[::200] + 50
mean = np.mean(transactions)
std = np.std(transactions)
threshold = 3 * std
# Flag anomalies
for tx in transactions:
if abs(tx - mean) > threshold:
print("Anomaly detected:", tx)
In this snippet, any transaction exceeding the threshold is printed as an anomaly. In a live system, each flagged event would publish an alert into our pipeline, triggering a human-in-the-loop review. More sophisticated ML models (e.g. isolation forests, clustering, neural networks) could be deployed within the same stream-processing layer. The result is immediate: anomalous trades or sensor readings are spotted as they happen, and regulators can see them on live dashboards.
UK Case Studies and Initiatives
This concept is not purely theoretical. UK regulators have begun experimenting with exactly these approaches. For instance, the FCA’s BLENDER project has long been blending multiple trading venue feeds to detect market abuse. BLENDER acts as “middleware” – it ingests streams from different exchanges, “blends” them into a unified dataset, and feeds them into the FCA’s surveillance tool. By consolidating data, BLENDER gives supervisors a holistic, real-time view of trading activity. It started as an inhouse pilot in 2013-2015, went live in 2017 on cloud infrastructure, and was updated in 2018 for MiFID II requirements. Today it is integral to FCA’s day-to-day market monitoring, enabling faster detection than was previously possible. BLENDER embodies the automation-first ethos: instead of auditors manually collating reports, the system continuously synthesises live data feeds.
Similarly, the FCA’s recent Strategy explicitly commits to investment in technology to handle its workload. As noted by the FCA’s CEO, “We will invest in our technology, people and systems to be more effective. We assess around 100,000 cases… every year. New approaches will allow us to better handle that significant caseload”. In practice this means building on BLENDER and other tools with AI enhancements. The FCA has also supported RegTech through its Innovation Sandbox and the Digital Sandbox, indicating a culture shift toward technical solutions (though those schemes focus on firms). On the regulatory side, the FCA’s guidance has cleared the way for cloud outsourcing, and its collaboration in the Digital Regulation Cooperation Forum (with Ofcom, ICO, CMA) fosters shared tech innovation.
Ofcom and spectrum regulation also illustrate this trend. Ofcom has invested in technology sandboxes (e.g. SONIC labs for Open RAN) and is actively “publishing large data sets to help train and develop AI models” in spectrum management. In 2024-25 the regulator trialled over a dozen AI proofs-of-concept to improve productivity and analytical capacity. Industry bodies likewise recognise AI’s potential in compliance: a TechUK report for the UK Spectrum Policy Forum highlights that AI-driven “anomaly detection methods for automated flagging of suspicious activity” could greatly improve proactive spectrum monitoring. (For example, drones or fixed sensors could stream RF measurements to a cloud service that automatically flags unexpected interference.) While these are not commercial products yet, they point to a future where Ofcom moves from human spot-checks to AI-assisted oversight.
In short, UK regulators are already on the path to automation-first. The FCA’s BLENDER and AI projects show one vision in financial markets. Ofcom’s AI strategy and TechUK’s recommendations illustrate how the same approach can apply to telecoms and broadcasting. Cross-agency initiatives (like the DRCF’s AI and Digital Hub) further indicate government support for applying AI to regulatory challenges. Policy documents repeatedly stress technology. For example, the FCA foreword notes that “by harnessing technological advances… our markets… will function better”. This is the logic behind the automation-first regulator: using data and cloud tech not only makes regulation more efficient, but ultimately leads to safer, more trustworthy markets.
Conclusion
Moving to an automation-first model is a major organisational shift for UK regulators, but it is rapidly becoming necessary and feasible. By adopting cloud-native, event-driven architectures with real-time analytics, bodies like the FCA and Ofcom can monitor compliance continuously instead of intermittently. In practice this means streaming in transaction, communications and sensor data; running live analytics and ML to detect anomalies; and automatically alerting supervisors the instant a concern arises. Pilot projects like the FCA’s BLENDER and Ofcom’s AI sandbox prove this approach can work in our regulatory context. Moreover, UK policy actively encourages it – regulators are exhorted to “improve our processes” with technology, and the Government’s Cloud First policy mandates cloud automation.
With these tools in place, regulators can intervene faster, catch novel forms of abuse, and focus on the highest-risk cases. It also has the benefit of reducing burdens: firms see fewer pointless reviews if regulators can automatically sift out the trivial cases. Ultimately, an automation-first regulator aligns with the UK’s goal of being a tech-savvy, innovation-friendly economy. By continuously analysing the “digital exhaust” of markets, UK regulators will be better equipped to protect consumers and market integrity in real time – essentially future-proofing our oversight in a fast-moving world.
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