RegTech, short for regulatory technology, is software that automates compliance work UK financial firms must perform to satisfy the Financial Conduct Authority (FCA), the Prudential Regulation Authority (PRA) and the Information Commissioner's Office (ICO). In practice it handles anti-money-laundering checks, identity verification, transaction monitoring, sanctions screening, Consumer Duty outcome tracking and regulatory reporting through RegData. The UK RegTech and compliance-automation market is worth roughly £5.5 billion in 2026, part of a global sector heading toward USD 116 billion by 2036 at around 19% annual growth. Well-deployed tools cut manual compliance cost by 30% to 50%, reduce false-positive alert volumes by up to 60% and shorten onboarding from days to minutes. The FCA actively encourages adoption through its regulatory sandbox, TechSprints and Innovation Hub. No tool is "FCA-approved", but the right stack makes your firm demonstrably compliant and audit-ready, which is what the regulator actually expects.
Last updated: June 2026
RegTech is the use of technology, increasingly artificial intelligence and machine learning, to help regulated firms meet their compliance obligations faster, cheaper and with a stronger audit trail than manual processes allow. It is a subset of FinTech, but where FinTech tends to mean customer-facing innovation like payments or lending, RegTech sits behind the scenes inside the compliance, risk and finance functions. The FCA itself coined much of the early momentum: its 2016 Call for Input (FS16/04) on supporting regulatory innovation effectively put the word on the UK map, and the regulator has backed it ever since because better compliance technology reduces systemic risk across the market it supervises.
The FCA cares because the cost and failure rate of manual compliance is a genuine threat to market integrity. Financial crime alone costs the UK economy tens of billions of pounds a year, and the regulator has handed out significant fines for AML control failures, weak transaction monitoring and poor customer outcomes. When a mid-sized bank runs a legacy monitoring system that generates a 95% false-positive rate, analysts drown, real suspicious activity slips through and the firm is one audit away from enforcement. RegTech is the FCA's preferred answer because it scales supervision without scaling headcount.
Our honest view: RegTech is no longer optional for any FCA-authorised firm processing meaningful transaction volume. The regulator's expectations on data quality, outcome evidence and resilience have risen faster than any compliance team can meet by hand. The firms that treat RegTech as a strategic capability rather than a grudging cost line are the ones passing supervisory reviews comfortably.
The UK is one of the most active RegTech markets in the world. Several factors drive this:
For a UK business owner running a regulated firm, the takeaway is simple. The regulator is not just tolerant of RegTech; it is steering you toward it. Building the right tooling now, ideally through a focused business process automation programme, is both a compliance investment and a defensive one.
RegTech maps cleanly onto almost every major FCA and adjacent regime, but the value comes from matching the right tool type to the right obligation rather than buying a single "compliance platform" and hoping. The most common mistake we see is firms purchasing broad suites that do everything adequately and nothing brilliantly. The better approach is to map each regulatory regime to the specific capability that discharges it, then assemble a focused stack. The table below is the practical reference our clients ask for most often.
| Regime / Obligation | What It Requires | RegTech Capability That Solves It |
|---|---|---|
| MLR 2017 / AML | Customer due diligence, ongoing monitoring, SAR filing | Automated KYC/identity verification, perpetual KYC, transaction monitoring |
| FCA Consumer Duty | Evidence of good customer outcomes across four areas | Outcome-monitoring analytics, communication testing, vulnerability detection |
| SMCR | Accountability mapping, fitness and propriety, conduct records | Responsibilities-mapping software, attestation and certification workflows |
| Operational Resilience (March 2025 rules) | Important business services, impact tolerances, scenario testing | Resilience-mapping platforms, third-party risk monitoring, incident tracking |
| Sanctions (OFSI) | Screening against UK sanctions lists in real time | Real-time sanctions and PEP screening with fuzzy matching |
| FCA / PRA Reporting | Accurate, timely data submission via RegData | Reporting automation, data-lineage and validation tooling |
| UK GDPR (ICO) | Lawful processing, DSAR handling, breach notification | Privacy-management platforms, automated DSAR fulfilment, consent logging |
| PSD2 / Open Banking | Strong customer authentication, secure data sharing | SCA orchestration, API security and consent management |
Reading this table, a pattern emerges. Almost every obligation reduces to one of four underlying technical jobs: verifying who someone is, watching what they do, proving you are treating them fairly, and reporting all of it accurately. RegTech tools cluster around those four jobs. Once you see your obligations through that lens, vendor selection becomes far less bewildering.
A word of caution worth stating plainly. DORA, the EU Digital Operational Resilience Act that took effect in January 2025, does not bind UK-only firms directly, but if you serve EU customers, use EU-based ICT providers or operate a group entity in the bloc, it reaches you anyway. Many UK firms have found DORA's requirements overlap heavily with the FCA's own operational resilience rules, so a single resilience-mapping investment often discharges both. Do not let a vendor scare you into double-buying.
The obligations that most frequently trigger a RegTech project, in our experience advising London firms, are AML monitoring and Consumer Duty evidence. Those two consume the most analyst time and carry the highest enforcement risk, so they deliver the fastest payback when automated.
RegTech handles AML by automating the three pillars of the Money Laundering Regulations 2017: knowing your customer at onboarding, screening continuously against sanctions and politically-exposed-person lists, and monitoring transactions for suspicious patterns throughout the relationship. Manual versions of these tasks are slow, inconsistent and expensive. Automated versions run in seconds, apply identical logic to every case and leave a complete audit trail the FCA can inspect.
At onboarding, modern KYC tooling verifies a customer's identity by checking a government-issued document against a live selfie using biometric matching, cross-referencing the details against sanctions and PEP databases, and pulling adverse-media signals. A process that once took a compliance officer two days of back-and-forth now completes in under three minutes. Perpetual KYC, the more advanced model, re-runs these checks automatically whenever a customer's risk profile changes rather than waiting for a calendar-driven annual review, which closes the gap criminals exploit between reviews.
Transaction monitoring is where AI earns its keep. Legacy systems use fixed rules: flag any transfer over £10,000, flag any payment to a high-risk jurisdiction. Criminals learn these thresholds and structure activity to stay below them. Machine-learning monitoring instead builds a behavioural baseline for each customer and flags deviations from that customer's own normal pattern, catching structuring, layering and mule activity that rule-based systems miss. Crucially, it slashes false positives. A well-tuned model can cut alert volumes by 50% to 60% while improving the true-positive rate, which means analysts investigate fewer, better-quality alerts.
| AML Function | Manual / Legacy Approach | RegTech Approach | Typical Improvement |
|---|---|---|---|
| Identity verification | Manual document checks, 1-2 days | Biometric + document AI, real time | Onboarding cut to under 3 minutes |
| Sanctions / PEP screening | Periodic batch checks | Real-time fuzzy-match screening | Continuous coverage, fewer misses |
| Transaction monitoring | Fixed-threshold rules | Behavioural ML models | False positives down 50-60% |
| SAR preparation | Manual narrative drafting | Assisted case packaging | Investigation time down 30-40% |
| Ongoing due diligence | Annual calendar reviews | Perpetual KYC, event-driven | Risk gap effectively closed |
The financial-crime slice of RegTech is the fastest-growing of all. The global financial-crime RegTech market sits at around USD 4.5 billion in 2025 and is forecast to reach USD 17.4 billion by 2032, a compound annual growth rate near 21%. That growth is not hype; it reflects regulatory pressure. UK AML enforcement has intensified, the MLR have been amended repeatedly, and the FCA expects firms to demonstrate that their monitoring is proportionate, risk-based and effective, not merely present.
Our stance here is firm. If your transaction monitoring still runs on static rules and your analysts spend most of their week clearing false positives, you are carrying both an efficiency problem and a regulatory risk. Many firms layer an AI automation capability over their existing core systems rather than ripping them out, which delivers the false-positive reduction without a multi-year platform migration. Be sceptical of any vendor that insists you must replace everything; the best results usually come from intelligent augmentation.
RegTech supports Consumer Duty by turning a principles-based obligation into measurable, evidenced data, and it supports operational resilience by mapping critical services, monitoring third parties and testing against disruption scenarios. Both regimes share a demand the FCA now makes of every firm: show me the evidence, not the policy document. RegTech exists to produce that evidence continuously rather than scrambling for it before a supervisory visit.
Consumer Duty, fully in force since 2023 for open and closed products, requires firms to deliver good outcomes across four areas: products and services, price and value, consumer understanding, and consumer support. The hard part is proving you have done so. RegTech tools ingest your customer-interaction data, communications and complaints, then surface metrics: are vulnerable customers getting worse outcomes? Are your communications actually understood, measured through comprehension testing and engagement analytics? Is your pricing delivering fair value across customer segments? An outcome-monitoring dashboard gives your board the evidence pack the FCA asks for and flags problem areas before they become harm.
Vulnerability detection deserves special mention. The FCA expects firms to identify and support customers in vulnerable circumstances, but vulnerability is often invisible in structured data. Natural-language tools can scan call transcripts and chat logs for indicators of financial difficulty, bereavement, health issues or low confidence, then route those customers to appropriate support. This is one of the highest-value RegTech use cases because it directly improves outcomes the regulator scrutinises most closely. Firms building this often pair it with an AI voice agent that can detect vulnerability cues in real time and escalate sensitively to a human.
Operational resilience became enforceable in its current strengthened form in March 2025. Firms must identify their important business services, set impact tolerances defining the maximum acceptable disruption, and demonstrate through scenario testing that they can stay within those tolerances. RegTech resilience platforms map dependencies across people, processes, technology and third parties, monitor the health of those dependencies in real time, and run simulated disruption scenarios so you can prove your tolerances hold.
| Consumer Duty Outcome Area | What You Must Evidence | RegTech Capability |
|---|---|---|
| Products and services | Products meet target-market needs | Target-market analytics, product-governance tracking |
| Price and value | Fair value across segments | Pricing fairness analysis, value-assessment dashboards |
| Consumer understanding | Communications are understood | Comprehension testing, readability and engagement analytics |
| Consumer support | Support is accessible and effective | Service-level monitoring, vulnerability detection, complaint analysis |
The connective tissue between Consumer Duty and operational resilience is data. Both regimes punish firms that cannot produce timely, accurate, joined-up information about how they operate and how customers fare. A firm that invests in clean data pipelines and continuous monitoring discharges both obligations from one foundation. That is why we counsel clients to treat their compliance data architecture as the real asset; the dashboards sit on top of it. Get the plumbing right, often through a custom CRM or data-integration project, and the evidence flows.
RegTech automates regulatory reporting by extracting data directly from source systems, validating it against the regulator's rules, and submitting it to the FCA's RegData portal or the PRA's collection systems without manual re-keying. Regulatory reporting is one of the most error-prone, labour-intensive jobs in any regulated firm, and it is precisely the kind of repetitive, rules-bound process automation excels at.
The FCA collects firm data through RegData, the platform that replaced the older GABRIEL system, and the PRA collects through its own channels. A typical authorised firm must submit dozens of returns a year covering capital, liquidity, conduct, financial-crime and client-money data. Done manually, each return involves analysts pulling figures from multiple systems into spreadsheets, reconciling them, formatting to the regulator's specification and uploading by a hard deadline. Mistakes are common, late submissions attract scrutiny, and the whole exercise consumes senior compliance time that could be spent on actual risk management.
Reporting automation removes the manual chain. The core capabilities are:
There is a forward-looking dimension worth noting. Regulators globally, and the FCA specifically through its work on digital regulatory reporting, are moving toward machine-readable rules and standards-based reporting where the regulation itself is expressed in code that systems can interpret directly. This is still maturing, but firms that build clean, well-structured data foundations now will adapt to it far more easily than those nursing spreadsheet-based processes. The honest rule is this: every hour you spend manually re-keying figures into a regulatory return is an hour of risk and cost you can design out.
| Reporting Stage | Manual Process | Automated Process | Benefit |
|---|---|---|---|
| Data gathering | Analysts pull from many systems | Direct system extraction | Hours saved, fewer errors |
| Reconciliation | Manual spreadsheet checks | Automated validation rules | Errors caught pre-submission |
| Formatting | Manual to regulator spec | Auto-formatted to taxonomy | No formatting rejections |
| Submission | Manual upload to RegData | Programmatic submission | No missed deadlines |
| Audit trail | Reconstructed after the fact | Captured automatically | Inspection-ready evidence |
For firms with complex back-office systems, reporting automation often pairs well with a broader ERP implementation that gives the business a single source of truth, eliminating the data fragmentation that makes reporting painful in the first place.
AI-based RegTech is better than legacy rule-based systems for detecting novel and adaptive risk, reducing false positives and scaling to high volumes, but rule-based logic still has a legitimate place for clear-cut, deterministic checks. The honest answer is that the strongest modern systems combine both: deterministic rules where the regulation is black and white, and machine learning where patterns are complex and evolving. Anyone selling you pure AI or insisting rules are obsolete is overselling.
Rule-based systems do exactly what they are told. They are transparent, easy to explain to a regulator and perfect for binary obligations: block any payment to a sanctioned entity, flag any transaction over a threshold. Their weakness is rigidity. They cannot spot a pattern they were not explicitly programmed to find, they generate enormous false-positive volumes because they cannot weigh context, and bad actors reverse-engineer their thresholds. A firm relying solely on rules is always fighting the last war.
Machine-learning systems learn from data. They build behavioural baselines, detect anomalies, weigh dozens of signals simultaneously and adapt as patterns shift. This is transformative for transaction monitoring and fraud detection, where the threat constantly evolves. The trade-off is explainability. The FCA and the ICO both expect firms to explain automated decisions affecting customers, so a model that produces an output no human can justify is a regulatory liability, not an asset. This is why explainable AI, models that surface the reasons behind their outputs, has become essential rather than optional in financial services.
Our considered view, after building these systems for UK firms, is that the future is real-time, embedded and explainable. Compliance is shifting from a periodic, after-the-fact check to a continuous control woven into the transaction flow itself. A payment is screened, scored and either passed or held in the moment it happens, not in a batch job overnight. AI makes that real-time model viable at scale. But every model decision that touches a customer must be defensible, logged and reviewable, or you have simply traded one regulatory problem for another.
| Dimension | Rule-Based Systems | AI / Machine Learning |
|---|---|---|
| Detecting known patterns | Excellent, transparent | Good, sometimes overkill |
| Detecting novel patterns | Poor | Excellent |
| False-positive rate | High | Substantially lower |
| Explainability | Naturally transparent | Requires explainable AI design |
| Maintenance | Manual rule updates | Model retraining and monitoring |
| Scalability | Degrades with volume | Scales well |
| Best for | Deterministic, binary checks | Behavioural, evolving risk |
The practical recommendation: do not frame this as AI versus rules. Frame it as using the right tool for each control. Keep rules for the things rules do well, deploy machine learning where pattern complexity defeats rules, and invest in explainability so every automated decision stands up to regulatory challenge. Firms that get this balance right report both lower compliance cost and stronger audit outcomes, which is the combination the FCA rewards.
You choose the right RegTech solution by starting from your specific regulatory obligations and risk profile rather than from a vendor's feature list, then evaluating against integration, explainability, data security, regulatory track record and total cost of ownership. The single biggest cause of failed RegTech projects we see is firms buying a shiny platform before they have defined the problem it must solve. Reverse that order and your odds improve dramatically.
Begin with a structured requirements exercise. Which obligations consume the most resource or carry the most risk for your firm? Where have past audits or near-misses exposed weakness? What does your data landscape actually look like, because a tool that cannot ingest your data cleanly will never deliver. Only once you have answered these should you look at vendors. The selection criteria that matter most:
Implementation is where good intentions meet reality. The most common pitfalls are underestimating data-quality work, treating the project as IT-only rather than a compliance-and-technology partnership, and going live without a parallel-run period to prove the new system matches or beats the old. Our strong advice is to pilot against historical data first. If you are replacing transaction monitoring, run the new model over last year's alerts and confirm it catches what mattered while cutting the noise. Evidence beats vendor promises every time.
| Selection Criterion | Question to Ask | Red Flag |
|---|---|---|
| Regulatory fit | Is this built for UK FCA regimes? | US-only product, vague on UK rules |
| Integration | How long to connect our systems? | Months of bespoke work required |
| Explainability | Can it justify every decision? | Black-box model with no rationale |
| Data security | Where is data stored and processed? | Unclear residency, weak certifications |
| Track record | Which UK firms have deployed it? | No comparable references |
| Total cost | What is the all-in 3-year cost? | Licence quoted in isolation |
For smaller firms, a frequent question is whether RegTech is even affordable. It is, increasingly. Modular, cloud-based and pay-as-you-grow pricing has put serious capability within reach of firms that could never have funded a traditional compliance platform. The honest rule for small firms is to buy narrow and deep: pick the one or two obligations that carry the most risk for your specific model and automate those brilliantly, rather than spreading a thin budget across a do-everything suite. A focused workflow automation build around onboarding and monitoring often delivers more value than an enterprise platform you will never fully use.
Softomate implements RegTech for UK financial firms through a five-stage process that starts with mapping your obligations and ends with a monitored, audit-ready system, typically delivered in 8 to 16 weeks depending on scope, with fixed-quote pricing agreed before any build begins. We are a London-based AI automation and software development agency in Stanmore, and we build compliance technology the way the FCA actually wants it: integrated, explainable and evidenced. We do not sell off-the-shelf platforms; we build and integrate the right capability around your existing systems.
Our approach rests on a principle worth stating: compliance technology must fit your firm, not the other way round. A generic platform forces your processes to bend to its assumptions. A purpose-built integration, often combining best-of-breed components with custom software development and automation, fits your obligations, your data and your risk appetite precisely. That is the difference between a tool you tolerate and a system that genuinely reduces cost and risk.
The five stages:
| Stage | Typical Duration | Key Deliverable |
|---|---|---|
| Discovery and mapping | 1-2 weeks | Obligation map, roadmap, fixed quote |
| Design and architecture | 1-2 weeks | Solution design and integration plan |
| Build and integration | 4-8 weeks | Working, integrated system |
| Pilot and validation | 2-3 weeks | Parallel-run evidence pack |
| Go-live and support | Ongoing | Live system, trained team, support |
On pricing, we work to fixed quotes agreed up front, never open-ended day rates that balloon. A focused single-obligation automation, for example an AI-driven onboarding and KYC flow, typically starts from around £9,000. A broader transaction-monitoring or Consumer Duty evidence build typically starts from around £18,000. A full multi-regime RegTech integration for a mid-sized firm generally runs from £35,000 upward depending on systems and complexity. Every engagement begins with a discovery session so the quote reflects your reality, not a guess. Many clients pair the compliance build with a customer-facing AI chatbot or web application so onboarding, support and compliance share one clean data spine.
The honest promise we make: we will not recommend technology you do not need. If a problem is better solved by a process change than a platform, we will tell you. That is how a compliance investment should work.
No, the FCA does not approve or certify specific RegTech products. It is the regulated firm, not the tool, that must be compliant. The FCA encourages RegTech through its sandbox and TechSprints, but you remain responsible for ensuring any tool you deploy actually meets your obligations and produces defensible, audit-ready evidence.
RegTech is technology used by regulated firms to meet their compliance obligations. SupTech, short for supervisory technology, is technology used by regulators themselves, such as the FCA, to supervise firms more effectively, analyse market data and detect risk. They are two sides of the same digital-compliance coin, and increasingly they speak the same data standards.
Yes. Modular, cloud-based and pay-as-you-grow pricing has made RegTech accessible to small firms. The smart approach is to automate the one or two obligations that carry the most risk for your model rather than buying a broad platform. A focused build can start from single-figure thousands of pounds and still deliver strong false-positive and onboarding-time improvements.
No, it augments them. RegTech removes repetitive, manual work like clearing false positives and re-keying reports, freeing compliance officers to focus on judgement-heavy tasks: investigating genuine risk, interpreting new rules and engaging the regulator. The FCA still expects accountable humans under SMCR; technology supports them rather than replacing the accountability.
RegTech turns Consumer Duty from a principles statement into measurable evidence. It monitors customer outcomes across products, price, understanding and support, detects vulnerable customers in interaction data, tests whether communications are understood, and produces the board-level evidence packs the FCA requests. This continuous evidence is far stronger than reconstructing data before a supervisory visit.
Perpetual KYC is an event-driven approach where customer due diligence refreshes automatically whenever a customer's risk profile changes, rather than waiting for a fixed annual review. It closes the gap criminals exploit between periodic reviews and keeps your AML picture continuously current, which the FCA increasingly expects for higher-risk relationships.
It can if deployed carelessly. The FCA and ICO require firms to explain automated decisions affecting customers, so black-box models without explainability become a liability. Deployed properly, with explainable AI, logged decisions and human oversight, AI reduces regulatory risk by improving detection and evidence. The key is designing for transparency from the start, not bolting it on later.
A focused single-obligation automation typically takes 6 to 8 weeks, while a broader multi-regime integration runs 12 to 16 weeks. The biggest variable is data quality and system integration, not the technology itself. A proper discovery phase that maps obligations and audits your data upfront prevents the delays that derail rushed projects.
DORA, the EU Digital Operational Resilience Act in force since January 2025, does not bind UK-only firms directly. However, it reaches you if you serve EU customers, use EU ICT providers or operate an EU group entity. Its requirements overlap heavily with the FCA's own operational resilience rules, so one resilience investment often satisfies both regimes.
Costs vary by scope. A focused KYC or monitoring automation can start from around £9,000, a Consumer Duty or transaction-monitoring build from around £18,000, and a full multi-regime integration from £35,000 upward. Always assess total cost of ownership, including integration, training and ongoing tuning, not just the licence or build fee in isolation.
RegTech has moved from optional to essential for UK financial firms. The market is worth around £5.5 billion domestically and heading toward USD 116 billion globally by 2036, driven by an FCA that actively steers firms toward better compliance technology through its sandbox, TechSprints and Innovation Hub. The value is concrete: onboarding cut from days to under three minutes, false positives reduced by 50% to 60%, manual compliance cost down 30% to 50%, and regulatory reporting that no longer depends on error-prone spreadsheets. The winning approach is not a single platform but a focused stack that maps each obligation, from MLR 2017 and Consumer Duty to operational resilience and FCA reporting, to the specific capability that discharges it, blending explainable AI with rules where each fits best. Start from your obligations and your data, pilot against real history, and insist on explainability. Do that, and compliance becomes a demonstrable strength rather than a recurring scramble before the next supervisory visit.
If you run an FCA-authorised firm and want to turn compliance from a cost centre into an audit-ready, automated capability, explore our business process automation services in London or get in touch for a fixed-quote discovery session.
Written by Deen Dayal Yadav, Founder of Softomate Solutions, a London-based AI automation and software development agency in Stanmore (HA7). With over 12 years building software, automation and compliance systems for UK businesses, including financial-services firms navigating FCA, PRA and ICO requirements, Deen leads a team that designs explainable, integrated RegTech rather than off-the-shelf platforms. Softomate Solutions is registered at Companies House. Learn more about our team and approach.
We protect the real names of all clients featured in examples and case studies. Every testimonial is from a real client.
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