RegTech Explained: How Technology Is Automating Financial Compliance

Compliance Is Eating Financial Services

The global financial services industry spends over $270 billion annually on compliance. That figure, from LexisNexis Risk Solutions and corroborated by multiple industry studies, includes personnel, technology, legal costs, and the operational overhead of maintaining regulatory programs across dozens of jurisdictions.

To contextualize what that number means: it is roughly equivalent to the combined annual revenue of Goldman Sachs and Morgan Stanley. The financial services industry spends as much keeping itself compliant as two of its most iconic institutions earn in total revenue.

And the spending keeps growing. The volume of regulatory change has been relentless since the 2008 financial crisis. Thomson Reuters tracked an average of 257 regulatory alerts per day in 2023 — changes to rules, guidance, enforcement actions, and interpretive letters that compliance teams must assess, interpret, and implement. A mid-sized bank operating in five jurisdictions must monitor thousands of distinct regulatory requirements simultaneously, across banking, securities, derivatives, privacy, consumer protection, and anti-money laundering regimes.

The traditional response has been headcount. When regulation increases, hire more compliance officers. When enforcement actions land, hire more compliance officers. The result is that compliance departments at major banks now number in the tens of thousands — JPMorgan Chase has disclosed a compliance and controls workforce exceeding 30,000 people.

RegTech — regulatory technology — exists because that model is breaking. The volume of regulation, the speed of regulatory change, and the complexity of cross-jurisdictional compliance have exceeded what human-scale compliance operations can handle effectively. The choice is not whether to automate compliance. The choice is how fast.

What RegTech Actually Is

RegTech is the application of technology — machine learning, natural language processing, cloud computing, distributed ledger technology, and advanced analytics — to regulatory compliance processes. That definition is deliberately broad because RegTech is not a single product category. It is a technology layer that sits across the entire compliance function.

The simplest way to understand the RegTech landscape is to map it against the compliance processes it automates.

The Six Categories of RegTech

1. Transaction Monitoring and AML

Transaction monitoring is where the largest compliance budgets are spent and where RegTech has the most immediate impact.

Traditional transaction monitoring systems use static rules: flag any transaction above $10,000, flag any wire to a high-risk jurisdiction, flag any pattern of transactions just below reporting thresholds (structuring). These rules were designed in the 1990s and have not fundamentally changed in decades. The result is a system that generates millions of alerts, of which roughly 95% are false positives that compliance analysts must manually review and dismiss.

The RegTech approach applies machine learning to the same data but with fundamentally different methodology. Instead of checking individual transactions against static rules, ML-based systems analyze behavioral patterns across the entire customer base.

Featurespace, spun out of Cambridge University's engineering department, uses adaptive behavioral analytics to create a dynamic profile for each customer. The system learns what normal behavior looks like for each individual and flags deviations from that baseline. A $50,000 wire from a customer who regularly processes six-figure transactions does not generate an alert. A $3,000 wire from a customer who has never made an international transfer does. The result is dramatically fewer false positives and more targeted detection of genuinely suspicious activity. Featurespace processes over 50 billion transactions annually across its client base, which includes HSBC, NatWest, and Worldpay.

Feedzai, founded in Portugal and now headquartered in the US, takes a similar approach with a focus on real-time payment fraud alongside AML. Feedzai's platform processes transactions in single-digit milliseconds — fast enough to score every transaction before it settles. Clients include Citibank, Lloyds Banking Group, and Standard Chartered.

NICE Actimize, part of NICE Systems (publicly traded, approximately $12 billion market cap), is the enterprise incumbent in this space. Its Suspicious Activity Monitoring platform is deployed at most Tier 1 global banks. NICE Actimize has been slower to adopt pure ML approaches than newer competitors, but its installed base and regulatory acceptance give it staying power that startups struggle to match.

The measurable impact is significant. Institutions that have deployed ML-based transaction monitoring report false positive reductions of 50-70%, according to case studies published by Featurespace and Feedzai. For a large bank processing 100,000 alerts per month, a 60% reduction in false positives eliminates 60,000 analyst review hours — the equivalent of roughly 350 full-time employees at standard analyst productivity rates.

2. Identity Verification and KYC

Identity verification technology has transformed what was once a multi-day manual process — submit documents, wait for review, potentially visit a branch — into a sub-five-minute digital experience.

The technology stack includes OCR for document data extraction, computer vision for document authentication, biometric face matching, liveness detection (active and passive), and database verification against credit bureaus, government databases, and sanctions lists.

Jumio, Onfido (now part of Entrust), Veriff, and Persona are the leading vendors. The market dynamics are shifting toward orchestration platforms — like Alloy and Persona — that combine multiple verification methods into configurable workflows rather than offering a single-point solution. A bank might use Jumio for document verification, iProov for liveness detection, and ComplyAdvantage for sanctions screening, all orchestrated through a single Alloy workflow.

3. Sanctions and Watchlist Screening

Sanctions screening is operationally critical and technically unforgiving. A missed sanctions match can result in penalties measured in billions of dollars — BNP Paribas paid $8.9 billion in 2014 for sanctions violations, Standard Chartered paid $1.1 billion in 2019.

The technical challenge is name matching. Sanctions lists contain names transliterated from Arabic, Chinese, Cyrillic, and other scripts, with multiple valid spellings. "Mohamed" can be spelled in dozens of ways across different transliteration standards. A simple exact-match approach would miss variant spellings; an overly broad fuzzy-match approach generates thousands of false positives on common names.

ComplyAdvantage uses machine learning and natural language processing to build dynamic risk profiles from sanctions lists, PEP databases, adverse media sources, and court records. Instead of matching names against a static list, ComplyAdvantage constructs entity profiles that include aliases, associated entities, and contextual risk signals. This reduces false positives while maintaining detection sensitivity.

Refinitiv World-Check (now part of the London Stock Exchange Group) is the legacy market leader, maintaining a database of over 5 million risk-relevant entity profiles compiled by a team of human analysts. The combination of human curation and automated screening gives World-Check a depth of coverage that pure-technology approaches struggle to match for complex, multi-jurisdictional entity structures.

4. Regulatory Reporting

Financial institutions must file hundreds of regulatory reports annually — capital adequacy reports, liquidity coverage reports, transaction reports, statistical returns — across multiple jurisdictions, each with its own formats, frequencies, and data requirements.

The traditional process is manual: extract data from core banking systems, aggregate it in spreadsheets, map it to regulatory templates, validate it, and submit it. This process is labor-intensive, error-prone, and expensive. A 2020 Boston Consulting Group study estimated that regulatory reporting consumes 10-15% of total compliance budgets at large banks.

Regnology (formerly BearingPoint RegTech) automates the full reporting chain — data extraction, validation, transformation, and submission — for over 7,000 financial institutions across 60 countries. The platform maintains templates for hundreds of regulatory report types and automatically updates them when regulations change.

Axiom (part of the Axiom Software Group) focuses on regulatory reporting for complex global banks, supporting Basel III/IV capital calculations, IFRS 9 provisioning, and stress testing. The platform integrates with core banking systems to pull source data, applies regulatory logic, and generates submission-ready reports.

Suade, a London-based RegTech, takes a "regulation-as-code" approach — encoding regulatory requirements as machine-readable rules that can be automatically applied to financial data. This approach makes regulatory changes easier to implement because the regulation itself is expressed in code rather than interpreted from legal text.

5. Regulatory Change Management

The volume of regulatory change is one of the hardest operational challenges in compliance. When the Basel Committee publishes new capital requirements, or the EU finalizes a new directive, or FinCEN issues new guidance, compliance teams must assess the impact, update policies, modify systems, retrain staff, and document everything. The traditional approach — teams of lawyers and compliance officers reading regulatory publications and manually mapping changes to internal controls — does not scale.

Ascent RegTech uses NLP to automatically read, classify, and map regulatory requirements to a financial institution's specific obligations. The platform maintains a dynamic regulatory inventory and alerts compliance teams when new requirements affect their business.

CUBE Global takes a similar approach, using AI to monitor regulatory publications across over 180 jurisdictions and automatically classify changes by topic, jurisdiction, and affected product. CUBE maintains a database of over 60,000 regulatory documents and provides impact assessments that help compliance teams prioritize which changes require immediate action.

The measurable benefit is speed. A regulatory change that might take a compliance team two weeks to assess, interpret, and implement can be flagged, classified, and mapped to affected controls within hours using NLP-based tools. The human judgment is still required — deciding how to respond is not automatable — but the information-gathering and classification steps can be largely automated.

6. Blockchain and Crypto Compliance

Cryptocurrency compliance is a distinct RegTech category because the underlying technology — public blockchains — creates unique compliance challenges and opportunities that traditional financial infrastructure does not present.

The challenge is pseudonymity. Bitcoin and Ethereum transactions are publicly visible on the blockchain but are linked to cryptographic addresses, not named individuals. Tracing the flow of funds from a ransomware payment through multiple wallets to an exchange where it is converted to fiat currency requires specialized analytical tools.

Chainalysis is the dominant player, used by over 100 government agencies and 1,300 private sector clients. Its Reactor tool visualizes transaction flows across multiple blockchains, identifies wallet clusters belonging to the same entity, and maps connections between known entities (exchanges, darknet markets, sanctioned wallets) and unknown addresses. Chainalysis's data powers the majority of cryptocurrency-related law enforcement investigations globally.

Elliptic, founded in London, provides similar blockchain analytics with a focus on financial institution compliance. Its platform screens cryptocurrency transactions against sanctions lists and risk typologies, enabling banks and payment processors to assess the risk of crypto-related transactions.

TRM Labs focuses on cross-chain analytics — tracing transactions that move across multiple blockchains using bridges, mixers, and cross-chain swap protocols. As DeFi (decentralized finance) has grown, the ability to trace funds across chains has become essential for compliance with the FATF Travel Rule, which requires virtual asset service providers to collect and transmit originator and beneficiary information for transactions above $1,000.

Why Adoption Is Slower Than It Should Be

If RegTech demonstrably reduces false positives, cuts compliance costs, and improves detection, why is adoption not universal?

The answer is structural, not technological.

Regulatory conservatism. Financial institutions are regulated entities, and their compliance programs must satisfy regulators during examinations. An examiner who understands rule-based transaction monitoring — because they have been evaluating it for 20 years — may be skeptical of an ML model that produces fewer alerts. "We generate fewer alerts because our model is more accurate" is a harder argument to make to a regulator than "we generate 100,000 alerts per month because our rules are comprehensive." The burden of proof falls on the institution to demonstrate that the new approach is at least as effective as the old one, and that burden is high.

Explainability requirements. When a financial institution files a Suspicious Activity Report, it must document the basis for the filing — what the suspicious activity was, how it was detected, and why it warrants reporting. An ML model that flags a transaction as suspicious but cannot explain why in terms a compliance officer can articulate in a SAR narrative is not operationally deployable. The explainability constraint limits the types of ML models that can be used in production.

Vendor risk. Banks are understandably cautious about entrusting compliance-critical functions to startup vendors. If a RegTech vendor's system produces a false negative — fails to flag a genuinely suspicious transaction — the regulatory penalty falls on the bank, not the vendor. A five-year-old startup with $50 million in funding is a different risk profile than an IBM or Oracle. That risk calculus makes banks default to established vendors, even when newer vendors offer demonstrably superior technology.

Integration complexity. Enterprise banks run on legacy technology stacks — mainframes, COBOL-based core systems, decades-old data warehouses. Integrating a modern, API-first RegTech solution into that infrastructure is not a plug-and-play exercise. It requires data mapping, middleware development, testing, and validation, often taking 12-18 months for a full production deployment.

Procurement cycles. Large banks have procurement processes designed for enterprise software contracts — RFPs, security assessments, legal review, proof-of-concept phases, and budget cycles that can stretch 12-24 months from initial evaluation to signed contract. RegTech startups with 18 months of runway cannot always survive that timeline.

The Opportunity: Compliance as Competitive Advantage

The standard framing of compliance is cost: regulatory overhead that must be minimized. That framing is wrong — or at least incomplete.

The financial institutions that will outperform over the next decade are the ones that treat compliance as a strategic capability, not a cost center. Here is why.

Faster onboarding. An institution that can verify identity, screen against sanctions lists, and assess risk in under five minutes will convert more customers than one that takes three days. In consumer fintech, where multiple providers compete for the same customer at the moment of need, onboarding speed is a directly measurable competitive advantage.

Lower cost of risk. An institution with superior transaction monitoring — fewer false positives, better detection of genuine threats — spends less on investigations, faces lower regulatory penalty risk, and can price financial products more competitively because its risk assessment is more accurate. Better compliance data leads to better credit decisions, which leads to lower loss rates, which leads to better unit economics.

Regulatory credibility. An institution that can demonstrate to regulators that it has invested in advanced compliance technology — and can show measurable improvements in detection rates and false positive reduction — builds regulatory goodwill that translates into faster product approvals, fewer examination findings, and greater latitude to innovate.

Market access. As financial services become more global, the ability to comply with multiple jurisdictions simultaneously becomes a prerequisite for growth. An institution using automated regulatory change management and configurable compliance workflows can enter new markets faster than one that must build compliance capabilities manually for each jurisdiction.

Goldman Sachs's Transaction Banking division, Stripe's compliance infrastructure, and Revolut's investment in automated KYC all reflect this thesis. They are not minimizing compliance costs. They are investing in compliance capabilities that create measurable business advantages.

The RegTech Market: Size and Trajectory

The global RegTech market was valued at approximately $12 billion in 2023 and is projected to reach $30-45 billion by 2028, depending on the source (Grand View Research, MarketsandMarkets, and Juniper Research have varying estimates). The growth drivers are clear: increasing regulatory complexity, rising compliance costs, and the demonstrated effectiveness of technology-driven approaches.

Investment activity reflects the opportunity. Notable funding rounds and acquisitions in recent years include:

• Chainalysis: Raised $170 million in 2022 at a $8.6 billion valuation, making it the most valuable standalone RegTech company.
• ComplyAdvantage: Raised $100+ million across multiple rounds, backed by Goldman Sachs and Ontario Teachers' Pension Plan.
• Onfido acquired by Entrust (2024): Consolidation of identity verification into broader digital identity platforms.
• Featurespace: Raised $37 million in Series D, deployed at tier-1 global banks.
• Sumsub: Raised $80 million in Series C (2024), expanding from identity verification into full compliance orchestration.

The market structure is maturing. Point solutions are giving way to platforms. Banks that once bought separate tools for transaction monitoring, KYC, sanctions screening, and regulatory reporting are increasingly seeking integrated platforms or orchestration layers that connect best-of-breed tools into unified compliance workflows.

What Comes Next

Three trends will define the next phase of RegTech.

Real-time compliance. The shift from batch processing to real-time is well underway in payments (RTP, FedNow) and is now extending to compliance. Transaction monitoring that operates on yesterday's data cannot detect today's fraud. The next generation of compliance systems will operate on streaming data, scoring transactions before settlement rather than after.

Regulation-as-code. The idea that regulatory requirements should be expressed in machine-readable formats — rather than as legal text that humans must interpret and manually implement — is gaining traction among regulators and industry participants. The Bank of England, the Monetary Authority of Singapore, and Australia's ASIC have all explored regulation-as-code pilots. If regulations are published in standardized, machine-readable formats, the entire compliance implementation chain accelerates dramatically.

AI-native compliance. Large language models are beginning to be applied to compliance functions that were previously resistant to automation — regulatory interpretation, SAR narrative drafting, compliance training, and policy documentation. The combination of LLMs for natural language understanding and traditional ML for pattern detection creates a compliance stack that is more capable than either approach alone. The regulatory acceptance of these tools is still developing, but the direction is clear.

Key Takeaways

• Financial services compliance costs exceed $270 billion annually and are growing. The traditional response — hiring more compliance officers — does not scale and does not improve detection quality.
• RegTech spans six core categories: transaction monitoring, identity verification, sanctions screening, regulatory reporting, regulatory change management, and blockchain analytics. Each addresses a distinct compliance process with measurable technology improvement.
• ML-based transaction monitoring reduces false positives by 50-70% compared to rule-based systems. For a large bank, that translates to eliminating tens of thousands of wasted investigation hours annually.
• Adoption is slower than the technology warrants because of regulatory conservatism, explainability requirements, vendor risk concerns, legacy integration complexity, and enterprise procurement cycles.
• The strategic reframe is compliance as competitive advantage: faster onboarding, lower cost of risk, regulatory credibility, and faster market access. The institutions investing in compliance technology are outperforming, not just cost-cutting.
• The RegTech market is projected to reach $30-45 billion by 2028, driven by regulatory complexity and demonstrated ROI. The market is consolidating from point solutions toward integrated platforms.

Related Reading

• Anti-Money Laundering for Executives: What You Actually Need to Know — AML is the largest single compliance cost and the primary demand driver for transaction monitoring RegTech.
• KYC and Identity Verification: The Technology Behind Know Your Customer — A deep dive into the identity verification technology stack that forms one of RegTech's largest categories.
• How Banks Make Money — and Why Fintech Is Taking Their Lunch — Understanding bank economics is essential context for why compliance costs are existential for smaller institutions and a strategic lever for larger ones.