RegTech, short for regulatory technology, refers to software tools and automated systems that help financial firms meet their regulatory obligations more efficiently, accurately, and cost-effectively. The term covers a wide range of applications: automated monitoring of trading activity for market abuse detection, AI-powered customer screening against sanctions lists, digital audit trail generation, horizon-scanning tools that track forthcoming rule changes, and continuous compliance testing against policy frameworks. For UK firms regulated by the FCA and the PRA, RegTech has moved from a niche interest to a practical necessity as the volume and complexity of regulatory requirements has grown substantially over the past decade.
The cost of compliance is significant. A 2023 survey by the UK Finance and technology analyst group found that mid-sized UK financial firms spend between 6 and 10 percent of their operating cost base on compliance activities, with manual processes accounting for the majority of that spend. RegTech solutions consistently demonstrate the ability to reduce that cost by 20 to 50 percent for specific compliance functions while simultaneously improving accuracy and audit-readiness. For firms looking to grow without proportionally expanding compliance headcount, this makes RegTech investment a financial decision as much as a regulatory one.
Softomate Solutions works with UK financial services businesses in London and across the country to build and integrate RegTech tools that fit their specific regulatory profile. This guide explains the most important FCA regulatory requirements that RegTech addresses, how the technology works in practice, and what firms should consider when selecting or commissioning RegTech solutions.
Not all regulatory obligations can be automated, but a substantial number of high-cost, high-volume compliance tasks are well-suited to software. The FCA's rulebook, the FCA Handbook, contains thousands of rules across dozens of sourcebooks. The following areas represent the highest-value RegTech opportunities for UK financial firms.
Anti-Money Laundering (AML) and Know Your Customer (KYC): the Money Laundering Regulations 2017, which transpose the EU's Fourth and Fifth Anti-Money Laundering Directives into UK law post-Brexit (retained with modifications), require firms to conduct customer due diligence at onboarding and ongoing monitoring throughout the relationship. Automated KYC platforms can verify identity documents, cross-reference names against PEPs (Politically Exposed Persons) and sanctions lists, assess transaction patterns for suspicion indicators, and generate Suspicious Activity Reports (SARs) for the National Crime Agency, all with far greater consistency and speed than manual processes.
Transaction monitoring: the FCA's Market Abuse Regulation (UK MAR) requires investment firms to monitor for and report suspicious transactions and orders that might constitute insider dealing or market manipulation. Rule-based monitoring systems can flag patterns that match market abuse typologies, reducing the volume of data that human compliance officers need to review.
Consumer Duty monitoring: the FCA's Consumer Duty, effective from July 2023, requires firms to assess and evidence whether retail customers are achieving good outcomes. This requires ongoing data collection and analysis across product performance, pricing fairness, customer understanding, and complaint patterns. RegTech dashboards that aggregate this data automatically, flag outliers, and generate the evidence pack the FCA expects are far more reliable than manual data pulls and spreadsheet analysis.
Regulatory reporting: firms report to the FCA via a range of submission mechanisms including REP-CRIM (anti-financial crime reports), Product and Service reports under Consumer Duty, and transaction reporting under UK MiFIR for investment firms. Automating data extraction, validation, and submission substantially reduces the risk of errors that attract FCA enquiries.
Operational resilience testing: PS21/3 requires firms to test that their Important Business Services can remain within impact tolerances during severe disruption. RegTech tools can automate the scheduling, execution, and evidence collection for these tests, replacing what is otherwise a significant manual programme of work.
Modern RegTech goes beyond rule-based monitoring. AI-powered compliance tools use machine learning to identify patterns that static rules miss, adapting to new typologies of financial crime, market abuse, or customer harm as they emerge. The technology has matured significantly since the early 2010s, and the FCA's 2021 AI and Machine Learning Discussion Paper acknowledged that AI in regulatory compliance is a legitimate and beneficial development when implemented with appropriate governance.
The most effective AI compliance applications in UK financial services today include:
Our AI process automation service includes the design and delivery of AI-powered compliance tools. We work with your compliance and technology teams to define the monitoring scope, build models appropriate to your data quality and volume, and put the human oversight frameworks in place that the FCA expects when firms deploy AI in consequential processes.
The FCA has consistently said that firms are responsible for the outcomes produced by their systems, regardless of whether those systems use AI or traditional rule-based logic. This is a critical point. Using an AI model to make a compliance decision does not transfer responsibility for that decision to the model vendor. The firm must be able to explain how the model works, how its outputs are used, what human oversight is in place, and how it is monitored for drift or bias.
The FCA's 2022 guidance on AI and machine learning in financial services, and subsequent work by the joint FCA-Bank of England AI Public-Private Forum, identified several key principles for compliant AI deployment in financial services. Explainability is paramount: if an AI model flags a transaction as suspicious or declines a customer application, the firm must be able to explain the reasoning in terms that a compliance officer and, if required, the FCA can understand. Black-box models with no interpretability layer are a regulatory risk.
Additionally, firms must monitor AI models for bias. If a transaction monitoring model has lower detection rates for a particular demographic group, that is a potential fairness issue under the Equality Act 2010 as well as a regulatory concern under Consumer Duty. Regular bias testing, documented and evidenced, is expected.
The ICO's guidance on AI and data protection adds further requirements. AI systems that make or contribute to automated decisions affecting individuals must comply with UK GDPR's provisions on automated decision-making, including the right to human review in certain circumstances.
A RegTech implementation that delivers real compliance value requires more than buying software. It requires mapping your current compliance obligations to your current processes, identifying where automation would reduce risk or cost, designing data flows that can feed the technology reliably, and building the governance framework that keeps regulators satisfied that the technology is working as intended.
The following sequence works well for UK financial firms approaching their first significant RegTech investment:
Step 1: Regulatory mapping. Document every material FCA and PRA requirement that applies to your business model. For each requirement, identify the current compliance process, who owns it, what data it requires, and what evidence it produces. This baseline is both the starting point for prioritisation and the foundation of your regulatory audit trail.
Step 2: Prioritise by risk and cost. Rank the compliance processes by the regulatory risk of failure (what is the FCA's likely response to a miss?) and by the cost of the current manual process. AML monitoring and Consumer Duty outcome testing consistently rank highest on both dimensions for most UK retail financial firms.
Step 3: Design the data architecture. RegTech tools are only as good as the data that feeds them. Before selecting any tool, assess whether your core systems produce the data the tool needs, in the format and frequency it needs it. Data quality problems are the most common reason RegTech implementations underdeliver.
Step 4: Build or buy? Established RegTech vendors (Behavox for communications surveillance, Actico for rules engines, Muinmos for regulatory onboarding, Clari5 for fraud) offer strong out-of-the-box capability for common use cases. Custom development makes sense where your compliance requirements are genuinely unusual, where you need deep integration with proprietary systems, or where vendor dependency is a commercial concern. Most firms use a hybrid: vendor tools for standard monitoring, custom-built dashboards and reporting layers for the FCA evidence that is unique to their business.
Step 5: Governance and review. Document how each RegTech tool works, what data it uses, what outputs it produces, and who reviews those outputs. Schedule quarterly reviews to assess whether the tool is still calibrated correctly. The FCA expects evidence that technology performs as intended, not just that it was deployed.
Our financial services software development practice works alongside legal and compliance advisers to deliver RegTech solutions that meet both technical and regulatory standards.
Three mistakes account for the majority of RegTech projects that disappoint.
The first is underestimating data quality requirements. RegTech tools require reliable, consistent, timely data. Firms that have not invested in data governance before a RegTech implementation find that the tool produces unreliable outputs because the underlying data is inconsistent across systems. The fix is usually more expensive than getting the data right first would have been.
The second is treating RegTech as a replacement for compliance expertise rather than a tool to augment it. A transaction monitoring system that flags 500 suspicious transactions per week is only valuable if there are trained humans reviewing those flags, making decisions, and taking the appropriate action. Compliance teams that cut headcount to fund the technology before it has proven its calibration accuracy often create a worse compliance position than before.
The third is failing to document the governance framework before the FCA asks for it. Regulators increasingly expect to see evidence that a firm knows how its compliance technology works, monitors it continuously, and has a plan for when it produces errors. Firms that cannot produce this documentation on request face a harder conversation with the FCA than those who proactively manage their RegTech governance.
Deploying RegTech is one thing. Demonstrating to the FCA that it works as intended is another. The FCA's supervisory approach is increasingly data-led: supervisors look for evidence of monitoring, outcomes data, and governance records rather than simply being told that a system is in place. Firms that treat RegTech as a black box, where something goes in and a result comes out, without documented governance, create a supervisory risk even when the technology itself is performing well.
Effective FCA engagement on RegTech typically requires a model card or system description for each RegTech tool, written in plain language that a non-technical supervisor can understand. This should cover what the tool does, what data it processes, how its outputs are used in decision-making, who reviews the outputs, what happens when it produces an unexpected result, and how its performance is monitored over time. FCA supervisors increasingly ask for these documents directly; having them ready avoids an urgent scramble when the request arrives.
Performance metrics must be tracked and reported internally. For a transaction monitoring system, this means tracking the detection rate (what proportion of confirmed fraud or AML events did the system flag?), the false positive rate (what proportion of flags turned out to be legitimate activity?), and the alert-to-action conversion rate (of the alerts that went to a human analyst, what proportion resulted in a SAR, a case, or an account action?). These metrics tell you whether the system is working; documenting them consistently gives the FCA the evidence it needs.
Annual model validation, conducted by someone other than the team responsible for the model, is best practice. This validation assesses whether the model is still appropriately calibrated for current fraud patterns and customer behaviour, tests for demographic bias, and reviews whether the training data still reflects the current operating environment. The validation report becomes part of the compliance documentation library and demonstrates the governance discipline that regulators expect.
For firms under FCA supervision with a named supervisor, proactive engagement is valuable. Sharing your RegTech governance framework with your supervisor before it is required, explaining how it works and what oversight is in place, positions your firm as well-governed rather than waiting to be asked. The FCA's Innovation Hub and TechSprint programmes also provide forums where firms can discuss RegTech approaches and get informal regulatory steers before committing to full implementation. Our AI process automation practice supports clients through both the technical implementation and the FCA engagement process.
Not directly in most cases. RegTech vendors that do not themselves conduct regulated activities are not FCA-authorised. However, the FCA expects regulated firms to conduct thorough due diligence on their technology providers under SYSC 8 (outsourcing rules) and PS21/3 (operational resilience). This means the firm bears responsibility for the RegTech tool's performance. A vendor that underdelivers does not give the firm a regulatory defence. Choose vendors who provide clear documentation, audit logs, and contractual SLAs that support your FCA obligations.
The FCA Innovation Hub (part of FCA Innovate) provides regulatory support to businesses that are developing genuinely innovative financial products or services. This includes access to the Regulatory Sandbox, where firms can test new products with real customers under a modified regulatory framework. The TechSprint programme, run by FCA Innovate, has specifically focused on RegTech challenges including AML and supervisory technology. If your RegTech solution involves novel approaches to compliance, engaging with FCA Innovate early can provide valuable regulatory clarity before a full launch.
A basic rule-based AML transaction monitoring implementation using an established vendor platform typically takes three to five months from vendor selection to live deployment, assuming data feeds are available and the compliance team is engaged throughout the configuration process. A custom-built AML monitoring solution with machine learning components takes six to twelve months. Adding a sanctions screening layer alongside AML monitoring adds two to four weeks if using the same data pipeline. The FCA's MLR2017 requires ongoing monitoring from the point of onboarding, so implementation timelines matter.
Yes, and it is increasingly seen as the practical way to meet Consumer Duty obligations at scale. The Consumer Duty requires firms to monitor customer outcomes continuously, not just at the point of sale. RegTech dashboards that aggregate product performance data, pricing fairness metrics, complaint rates, and customer understanding indicators give compliance teams a live view of outcomes and an automatically generated evidence pack for FCA review. Without technology, Consumer Duty monitoring for a retail firm with thousands of customers is practically impossible to do thoroughly with manual processes alone.
UK GDPR (the UK's post-Brexit version of the EU's General Data Protection Regulation) sets out the legal framework for processing personal data, including rights for individuals subject to automated decisions and requirements for Data Protection Impact Assessments (DPIAs) for high-risk processing. The ICO's AI guidance provides practical interpretation of how UK GDPR applies specifically to AI systems, covering explainability, bias testing, and human oversight requirements. For a RegTech deployment that uses AI to process personal data (as most AML and fraud tools do), both apply together. The DPIA is typically the starting point, and the ICO's AI guidance informs how you design the governance framework within the DPIA.
More from the blog
Let us help
Talk to our London-based team about how we can build the AI software, automation, or bespoke development tailored to your needs.
Softomate Solutions Limited
Deen Dayal Yadav
Online
