Regtech use cases: 7 applications in the financial sector
Regtech stopped being a category decision and became an operating requirement somewhere around the point where regulations started changing faster than compliance teams could read them.
The question now is not whether to use regtech, it is which parts of a compliance operation to hand to software, what to look for when choosing it, and how much of the work it can actually take off your team.
Below: the seven use cases that matter in financial services, what has changed as AI moved into each one, and a practical guide to selecting and integrating a platform.
What is regtech?
Regtech, short for regulatory technology, is software that automates and enhances regulatory processes. It appears in healthcare, AI, and big data, but its densest use is in financial services, across regulatory monitoring, transaction monitoring, reporting, case management, and compliance.
The economics are straightforward. Manually maintaining compliance is expensive and slow. Teams spend hours researching regulations and reviewing activity, which is time not spent on investigations or prevention strategy, and suspicious activity still gets missed.
The 7 regtech use cases in financial services
1. Identity verification and management
Digital identity verification is table stakes for any modern financial institution. Regtech software collects, stores, and verifies customer data through KYC and KYB procedures to confirm you are dealing with a real person, and only that person.
Identity theft remains one of the most common attack routes, because a handful of data points can be enough to open an account or access an existing one. So modern verification goes beyond matching a record. Digital identity verification compares live images against government-issued documents, with no physical interaction required.
Link analysis adds another layer, mapping the relationships between parties in a transaction or series of transactions. Understanding who is connected to whom is often what separates a genuine case from a false positive.
For fintechs specifically, this is where regtech earns its place fastest. Onboarding workflows can integrate KYC data partners directly, comparing what a new user submits against formal documents without a manual review queue. The result is customer due diligence that runs at signup speed rather than gating it.
2. Regulatory compliance and change management
Institutions have to track the regulations applying to them across every jurisdiction and product line they operate in. At current pace, doing that manually is no longer realistic.
Response time is the pressure point. Once a rule is revised, you can be in violation within weeks. GDPR is the standard illustration: overnight, long-standing practices around collecting and using data became prohibited, and every business touching EU users had to change how it operated.
The pattern has repeated. The EU's AML package and the establishment of a dedicated EU anti-money laundering authority, the Corporate Transparency Act's beneficial ownership requirements, MiCA for crypto assets, and the 2026 Nacha operating rule changes have each landed with implementation deadlines rather than gradual phase-ins. Without automated regulatory tracking, you find out late.
3. Regulatory reporting and case management
Reporting requirements differ by regulator and change constantly. Maintaining records and producing them on request used to consume resources that would otherwise go to prevention.
Compliance cost remains one of the largest line items in a risk function, and the majority of it is labor. Regtech reduces that by storing far more data than manual processes could, and presenting it in whatever format a given regulator requires.
The bigger gain is in the investigation itself. AML analysts have historically spent the large majority of their time gathering data for a case and a small minority actually investigating it. That ratio is what regtech, and now AI agents, are aimed at inverting. Unit21 customer DriveWealth files SARs 66% faster as a result.
4. Risk analysis and management
Risk analysis for financial crime is hard because it traditionally means using known past infractions to identify what is happening now, with an expectation that a diligent institution could have prevented it via a proper risk assessment.
Without breaking activity down into stages, indicators, and typical patterns, prediction is close to impossible. Manual behavior monitoring tends to surface warning signs after the loss rather than before.
FATF Recommendations require a risk-based approach, with additional vigilance calibrated to specific threat levels: the jurisdiction involved, terrorist financing exposure, and whether a politically exposed person is party to the activity. The response has to match the threat, which means more effort validating source of funds and continued monitoring even where nothing has gone wrong before.
Regtech analyzes activity and behavior to forecast likely outcomes rather than only recording them, and applies sustained vigilance to elevated-risk cases without adding headcount.
5. Transaction monitoring and screening
FATF requires prompt formal reporting of suspicious activity, which makes manual monitoring structurally inadequate. Manual review cannot happen in real time, and automated transaction monitoring can.
Rule-based monitoring can be configured against the specific factors that matter to your business, and thresholds tuned to your own risk profile rather than a vendor default. On instant payment rails this has become urgent rather than preferable: money moved through FedNow, RTP, or Zelle is unrecoverable within seconds, so real-time evaluation is the difference between blocking a payment and filing a report about one.
Screening is a distinct control. Payment screening and sanctions screening check transactions and counterparties against lists before completion, answering whether a transaction is permitted rather than whether it is consistent with a customer's behavior.
6. AML compliance and detection
AML fines have escalated sharply. TD Bank's roughly $3.09 billion penalty in October 2024 was the largest ever imposed under the Bank Secrecy Act, and it was for an inadequate monitoring program rather than for laundering. Fourteen years earlier, the record penalty was $160 million.
The finding in that case is instructive for anyone evaluating regtech: the bank's monitoring scenarios went unchanged for eight years, and 92% of its transaction volume was not monitored. Having a system is not the same as having coverage.
Even when laundering is stopped, suspicious activity reports still have to be filed. Failing to report a suspicious transaction, or investigating one insufficiently, is itself a violation.
Regtech and AML technology process this volume in real time, reducing investigation times and false positives. Intuit reduced investigation alert time by 65%, freeing investigators to investigate.
7. Fraud detection and prevention
Attacks come through payment fraud, account takeover, ACH fraud, and social engineering. With that many vectors, fraud detection and prevention has to identify and stop threats rather than only record them.
Monitoring also flags likely successful attempts so you can investigate and report. What distinguishes a strong fraud solution is whether your team can adapt detection logic as fraudsters change tactics, which they do faster than a quarterly release cycle allows.
How to choose regtech software
Six questions that reveal more than a feature list.
- Can my team write and modify a rule without vendor help, and how long does it take? Ask for a live demonstration. This is the single biggest determinant of whether the platform keeps working as your risk changes.
- Can I test a change against historical data before it goes live? Without this, every adjustment is an experiment on your analysts.
- When an alert fires, can you show me exactly why, in terms an examiner accepts? A system that cannot be explained is a regulatory liability regardless of accuracy.
- How does it handle a customer with activity across multiple accounts or entities? Most real cases only make sense at the entity level.
- What happens when we add a payment rail or launch a product? Does the data model absorb it, or is it an implementation project?
- What does the audit trail capture, and can we export it?
For more, see our guides on choosing transaction monitoring software and the common barriers to implementing regtech.
Buy or build?
You can do either, and most institutions that build underestimate the maintenance. A compliance system is never finished: regulations change, typologies change, products launch, and each one is engineering work competing against your product roadmap.
Purchasing a platform built for risk and compliance is usually the better trade, provided it does not lock your detection logic behind a vendor ticket. That is the failure mode that makes bought systems feel worse than built ones, and it is worth testing for specifically during evaluation.
Integration
The practical constraint is rarely the platform, it is your data. What determines the timeline is mapping transaction and customer data, agreeing the risk assessment that drives your rules, and calibrating thresholds against your own history.
A platform that is data-agnostic, able to interpret signals across any payment rail without a bespoke schema per source, shortens this considerably. Check what a platform integrates with before you evaluate features, and see how it fits the rest of your compliance tech stack.
Where regtech is heading
The category is shifting from software that generates work to software that does it.
Most regtech historically produced alerts for humans to process. The current generation of AI agents runs the investigation itself: gathering evidence, mapping entity networks, drafting narratives, and preparing filings, with a human reviewing and deciding. That changes the economics of a compliance function rather than just its efficiency.
The requirement that comes with it is explainability. An agent that reaches the right answer opaquely is worse than a rule that reaches a slightly worse answer readably, because you can defend the second one. Any evaluation of AI-driven regtech should start with the audit trail rather than the accuracy claim.
Regtech is also well suited to crypto and virtual asset businesses, where the regulatory picture moves fastest and manual tracking is least viable.
Regtech with Unit21
Unit21 covers all seven use cases above on one platform: identity and entity resolution, transaction monitoring, screening, case management, customer risk rating, and regulatory filing.
Three things distinguish it. Detection logic is written and tuned by your team in a no-code interface, testable against historical data before deployment. Every decision is explicit and auditable rather than an opaque score, with agent actions logged down to which configuration was running at the time. And AI agents handle the investigation assembly, so analyst time goes to judgment instead of collection.
For a deeper look, see our practitioner's guide to agentic AI.
Frequently asked questions
What is regtech?
Regtech, or regulatory technology, is software that automates and improves regulatory compliance processes. In financial services it covers identity verification, regulatory change tracking, reporting, risk analysis, transaction monitoring, AML detection, and fraud prevention.
What are the main use cases for regtech?
Identity verification and management, regulatory change management, regulatory reporting and case management, risk analysis, transaction monitoring and screening, AML compliance and detection, and fraud detection and prevention. Most institutions start with one and expand.
How is regtech different from fintech?
Fintech delivers financial products and services. Regtech helps the institutions delivering them meet their regulatory obligations. Many fintechs are significant regtech buyers, either directly or through a sponsor bank relationship.
Is regtech only for large banks?
No. Smaller institutions and fintechs often see a larger proportional benefit, because they face comparable regulatory obligations with far fewer people. The constraint is usually implementation capacity rather than budget.
Should we buy or build a compliance system?
Buying is usually the better trade, because a compliance system is never finished and maintenance competes with your product roadmap. The exception to watch for is a bought platform that locks detection logic behind vendor tickets, which removes the main advantage of buying.
How long does regtech implementation take?
It depends on data readiness more than the platform. Mapping your transaction and customer data, agreeing the risk assessment that drives your rules, and calibrating thresholds are what set the timeline. Institutions arriving with a documented risk assessment move considerably faster.
Does regtech replace compliance staff?
No. It removes the data-gathering work that consumes most analyst time. Decisions and accountability for filings stay with humans, and regulators expect it that way.
Gal Perelman is the Product Marketing Lead at Unit21, where she spearheads go-to-market strategies for AI-driven risk and compliance solutions. With over a decade of experience in the fintech and fraud sectors, she has led high-impact launches for products like Watchlist Screening and AI Rule Recommendations.
Previously, Gal held marketing leadership roles at Design Pickle, Sightfull, and Lusha. She holds a Master’s degree from American University and a Bachelor’s from UCLA, and is dedicated to helping banks and fintechs navigate complex regulatory landscapes through innovative technology.
