Smarter Compliance for Fintech Growth
A Strategic Blueprint for U.S. Fintechs Navigating AML and FCC Pressures Part One
Executive Summary
David Shapiro
Regulatory Affairs, US
Fintechs have redefined the speed and convenience of financial services. But with innovation comes responsibility—and regulators are watching closely. As digital-native players challenge legacy institutions, U.S. regulators are making it clear: fintechs offering money transmission services must meet the same rigorous compliance standards as traditional banks.
From the Bank Secrecy Act (BSA) and FinCEN priorities, to CFPB oversight and multi-state coordination, the compliance bar is rising. And the stakes are high. Civil penalties, partner de-risking, and reputational harm can derail even the most promising growth stories.
Yet compliance doesn’t have to be a growth inhibitor. With the rise of Cognitive AI, these fintechs now have the tools to detect risks earlier, reduce false positives, and scale smarter—without necessarily having to do a costly overhaul of their entire tech stack
This white paper is presented in two parts. Part 1 explores:
- The drivers of regulatory change
- Risks and costs of underperformance, and
- Foundational strategies for modernising AML and financial crime compliance.
Part 2 will build on this foundation—diving into:
- Advanced implementation models
- Governance best practices, and
- Scaling approaches that allow fintechs to meet rising expectations while unlocking growth opportunities.
AI isn’t just a technical upgrade—it’s a competitive advantage.
As fintechs continue to scale and capture market share, they’re also attracting more scrutiny, from regulators and bad actors alike. State agencies such as the New York Department of Financial Services (NYDFS) and the California Department of Financial Protection and Innovation (DFPI) and federal regulators, like FinCEN, the CFPB, and the OCC, are moving swiftly to apply bank-grade scrutiny. What was once a regulatory gray area is now a high-expectation zone.
It has been made clear: fintechs offering money transmission services must meet the same anti-money laundering (AML) standards as traditional banks. Recent enforcement actions demonstrate that rapid growth without strong controls can lead to severe financial and reputational fallout.
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1
Block Inc. (Cash App)
In 2024, Block Inc. agreed to pay $80 million in a multi-state settlement after regulators found significant gaps in AML monitoring, including insufficient systems for handling scale1. Separately, the New York Department of Financial Services (NYDFS) fined the company $40 million for failures in Bitcoin related KYC processes and risk oversight. -
2
Wise US
The cross-border payments firm paid $4.2 million across six states for AML/CFT failures between 2022-2023, including delayed Suspicious Activity Reports (SARs), underpowered program reviews, and insufficient oversight of transaction monitoring processes. -
3
OKX (Aux Cayes FinTech Co.)
OKX pleaded guilty to AML violations, admitting it had facilitated over $5 billion in suspicious activity linked to U.S. entities up until 2023. The Department of Justice imposed penalties exceeding $505 million, sending a clear message that crypto-fintech boundaries are firmly within regulatory reach.
U.S. fintechs today face a paradox: the need to accelerate growth while operating under increasingly stringent regulatory expectations. Compliance headcounts remain lean, onboarding speed is a competitive necessity, and regulators anticipate investigation times to shrink, not expand. Efficiency and effectiveness are no longer “nice-to-haves” in AML and FCC programs—they’re strategic levers for market differentiation and regulatory resilience.
Efficiency As A Strategic Lever
As fintechs grow in size and scope—handling everything from real-time payments to cross-border crypto transactions—they’ve come squarely into the spotlight of U.S. regulators. Yet many fintechs are still relying on compliance infrastructure built for traditional banks: manual alert reviews, disconnected customer risk data, and static rule-based monitoring. These tools aren’t just inefficient, they’re ineffective at uncovering complex, evolving financial crime typologies like mule networks and transaction layering.
Regulators are responding. FinCEN’s recent statements, as well as enforcement actions like those against Cash App (Block) and Wise US, make it clear: fintechs must demonstrate program maturity, alert quality, and timely reporting that rivals traditional financial institutions.
At the same time, compliance teams face a painful tradeoff—growing volumes of alerts without the analyst capacity to triage them efficiently. As noted in a recent Celent report, AML compliance now represents the largest compliance cost category, with many fintechs struggling to scale operations without slowing growth or increasing friction.
This is where efficiency becomes mission-critical. Investing in technology that uses AI,
at detection level, not just at alert level can not only lower costs, but can:
- Reduce false positives that waste analyst time and delay SAR filings
- Automate manual triage while preserving explainability
- Integrate fragmented case management and monitoring tools
- Shorten onboarding delays caused by manual KYC checks
- Deliver consistent intelligence to satisfy audit and regulatory review
Effectiveness For Risk Visibility
Efficiency may keep costs down—but it’s effectiveness that protects U.S. fintechs from enforcement action, reputational harm, and regulatory scrutiny. As financial crime grows more sophisticated, the real challenge for fintechs is building the capability to detect the risks that don’t fit legacy rules, exploit fast-moving payment rails, and shift dynamically across digital channels.
True effectiveness means finding a solution that can significantly enhance risk visibility by:
- Uncovering hidden anomalies or suspicious behaviors missed by static rules engines, including nuanced or complex typologies that have never been seen before.
- Fusing multiple data sources— transaction records, behavioral analytics, KYC/CDD data, and network link analysis—into a single, multidimensional risk picture.
- Adapting dynamically to evolving threat patterns without the delays and manual retuning cycles of traditional models.
This approach directly aligns with the Financial Action Task Force (FATF) mandate for a risk-based allocation of compliance resources, ensuring investigative capacity is focused on the most material risks. It also reflects the Wolfsberg Group’s push for AI- and analytics-enabled monitoring frameworks that raise the productivity and regulatory value of Suspicious Activity Reports (SARs) and Suspicious Transaction Reports (STRs).
Recent research from Accenture underscores the business upside—firms applying AI in risk management see a 35% improvement in identifying new growth opportunities compared to peers7. For fintechs, that means a compliance program that not only protects against regulatory and reputational risk but also supports smarter customer segmentation, faster market entry, and stronger banking partner trust.
Regulatory Alignment and External Perspective
Over a decade ago, the Financial Action Task Force (FATF) introduced the risk-based approach as a global standard—urging institutions to direct resources toward the most material risks. More recently, the Wolfsberg Group’s 2024 Statement on Monitoring for Suspicious Activity underscored the same imperative: modernize monitoring with data-driven, risk-based frameworks that prioritize value over volume of alerts.
The Wolfsberg Group calls for institutions to measure effectiveness not by the number of alerts or SARs filed, but by the quality and impact of the intelligence generated. This includes evaluating productivity, materiality, and risk coverage. For fintechs under increasing U.S. scrutiny, this means that alert precision, model transparency, and alignment to typologies matter more than ever.
In parallel, the U.S. Treasury’s 2024 Illicit Finance Risk Assessment spotlighted fintechs—including crypto and BaaS platforms—as critical actors in the financial crime ecosystem. The report emphasized the need for enhanced detection capabilities, real-time monitoring, and advanced analytics to address growing risk vectors like cross-border flows, instant payments, and platform layering.
Why This Matters for U.S. Fintechs
The U.S. regulatory landscape—driven by FinCEN, OCC, and state-level bodies—is converging on higher expectations for fintech FCC programs, particularly around suspicious activity detection and reporting timelines. Failures to detect, investigate, and report material threats in time have already led to multi-million dollar fines for high-profile fintechs as outlined above – often far more than the investment required for a solution to enable regulatory compliance in the first place.
In this environment, a dual focus on efficiency and effectiveness isn’t optional. Fintechs that master both can
- Maintain compliance headroom as transaction volumes grow.
- Respond to regulator inquiries with speed and precision.
- Use FCC as a trust-building advantage with customers, partners, and investors.
AML compliance is no longer a back-office obligation, it’s a business-critical function tied directly to customer experience, market access, and long-term growth.
Why Better Risk Coverage Drives Growth
Effective AML and FCC programs don’t just protect against enforcement, they enhance platform performance. Fintechs that invest in AI-powered compliance gain more than detection accuracy—they unlock frictionless onboarding, faster customer reviews, and more confident expansion into new product lines or geographies.
Take, for example, the customer journey. Manual reviews and inconsistent risk scoring delay onboarding, frustrate users, and create drop-off points. But AI can surface high-risk patterns and false positives with speed and precision, enabling smoother onboarding and faster decisioning. That creates a better user experience — and, in turn, stronger trust and retention.
Recent studies support the link between compliance innovation and growth. A Deloitte analysis found that financial institutions using advanced AI for compliance reported a 20% increase in customer satisfaction, correlating with a 15% revenue uplift8. Fintechs that deliver secure, seamless, and fast service are more likely to earn customer loyalty, and convert that loyalty into product adoption and lifetime value.
Trust Is the Growth Multiplier
Trust is the foundation of fintechs. Whether dealing with partner banks, regulators, or end users, institutions that can demonstrate robust AML controls gain the credibility needed to operate with fewer constraints.
Consider the growing trend of bank-fintech partnerships. Institutions with strong compliance frameworks are more likely to retain or win banking-as-a-service (BaaS) relationships. In contrast, poor AML performance can result in derisking, lost licenses, or reputational fallout—as seen in several high-profile regulatory actions over the past few years.
And it’s not just anecdotal: a recent Capgemini study showed that 95% of financial executives believe their legacy systems are holding back customer-centric growth. Modernizing compliance infrastructure, especially through AI, enables fintechs to unlock their full revenue potential.
From Regulatory Burden to Competitive Edge
For fintechs, user experience is king, however, long onboarding queues, excessive documentation checks, and slow transaction reviews erode customer trust and lead to churn.
By investing in comprehensive, AI-enabled risk detection, fintechs can:
- Accelerate onboarding—without compromising controls—but with dynamic risk ratings
- Allow low-risk users to onboard faster, lowering the investigation burden
- Build partner and regulator confidence with explainable and audit-worthy decisions
- Expand quickly with new products or into new jurisdictions with effective risk coverage
- Strengthen customer loyalty through secure, seamless services
Put simply: better risk detection doesn’t just reduce exposure, it fuels growth.
In a landscape where compliance missteps can halt momentum, fintechs that prioritize effectiveness will outperform, not just in compliance reviews, but in market share, reputation, and resilience.
A. Streamlining Success—Shift4 Transforms AML Compliance 
Case Study
Shift4, a global leader in payment technology, processes over $200 billion annually for more than 200,000 customers. Their ecosystem encompasses in-person and online payments, mobile and contactless technology, point-of-sales solutions, and robust business intelligence tools.
Background
Shift4 sought a comprehensive solution to streamline its AML compliance and transaction monitoring processes. With clients spanning industries as diverse as hospitality, retail, transit, sports, and entertainment, the company needed a partner who could both handle high transaction volumes and understand the unique risks within the payment industry.
AI-Driven Effectiveness for Complex Risk Coverage
Shift4 set out to modernize its approach to transaction monitoring by addressing four critical priorities: managing immense data volumes, enhancing risk detection, improving operational efficiency, and extending retention windows for long-term pattern analysis. With thousands of transactions processed every second, the need for a system that could scale without sacrificing insight was essential. Traditional rules-based engines were no longer sufficient—Shift4 required the ability to detect nuanced, evolving risks and reduce noise from false positives to allow analysts to focus on what truly matters. Additionally, expanding the data retention period was key to spotting subtle behavioral changes over time, ensuring a more complete picture of risk.
“We chose ThetaRay for its standout features, particularly its
AI model and robust reporting capability. They understand our
business and are great at adjusting their tools to fit our needs.”
Elina Jefremova, AML Manager, Shift4
ThetaRay’s Cognitive AI platform had a transformative effect on Shift4’s AML operations. By enhancing detection capabilities, Shift4 was able to move beyond static rules-based monitoring and identify complex transactional patterns and hidden risks with greater precision. The platform’s ability to monitor long-tail behavioral trends over extended timeframes significantly strengthened the company’s compliance posture and delivered greater visibility into evolving typologies. Importantly, this enhanced detection didn’t come at the cost of efficiency—by dramatically reducing false positives, the solution freed Shift4’s lean AML team to focus solely on genuine risk, improving SAR productivity and reducing investigative workloads.
Beyond detection, ThetaRay’s solution delivered tangible operational and strategic gains. Shift4 experienced a substantial drop in alert volumes for acceptable customer behaviors, directly reducing time and cost associated with manual reviews. This optimization translated into measurable savings and greater capacity to scale as transaction volumes increased. In addition, the system’s responsiveness enabled faster identification of potential threats—bolstering proactive risk management and reducing exposure to regulatory penalties. As a result, Shift4 not only improved day-to-day compliance performance but also solidified its reputation as a forward thinking payment processor equipped to thrive in a fast-evolving regulatory environment.
“Since implementing the ThetaRay solution, we have reduced our number of alerts for known and acceptable behaviors, which allows us to focus on investigating what truly matters”
The collaboration between Shift4 and ThetaRay demonstrates how advanced AI can transform compliance from a burden into a business enabler. By adopting ThetaRay’s Cognitive AI, Shift4 tackled immediate pain points and laid a foundation for scalable, adaptive compliance in an increasingly demanding regulatory environment. This case not only highlights the ROI of intelligent detection but also reinforces the strategic importance of compliance as a driver of resilience, customer trust, and future growth.
Part 1 has shown that fintech compliance is no longer a regulatory checkbox—it is a critical driver of resilience, trust, and sustainable growth. With state and federal regulators applying bank-grade scrutiny, and high-profile enforcement actions making headlines, the stakes have never been higher. Rapid growth without robust controls can lead to severe financial, reputational, and operational consequences.
The case for change is clear: manual, siloed, and reactive compliance frameworks are no longer fit for purpose. Instead, fintechs must adopt intelligence-led, explainable, and proportionate approaches that satisfy regulators while enabling innovation and inclusion.
In Part 2, we move from the why to the how—exploring advanced technology adoption, governance best practices, and scalable models that enable fintechs to meet rising expectations while turning compliance into a competitive advantage.
See part 2 here
Glossary
AI – Artificial Intelligence
The simulation of human intelligence processes by machines, especially computer systems, used in fintech for tasks like anomaly detection and transaction monitoring.
AFC – Anti-Financial Crime
A collective term for measures, processes, and systems designed to prevent, detect, and respond to financial crimes such as money laundering, fraud, and terrorist financing.
AML – Anti-Money Laundering
Regulations, laws, and processes designed to detect and prevent the laundering of illicit funds.
AMLA 2020 – Anti-Money Laundering Act of 2020
A US law that expands and modernizes the Bank Secrecy Act framework, emphasizing beneficial ownership transparency and risk-based approaches.
BaaS – Banking-as-a-Service
A model that allows fintechs to offer banking products and services by leveraging the licensed infrastructure of partner banks.
BSA – Bank Secrecy Act
US law requiring financial institutions to assist government agencies in detecting and preventing money laundering, including reporting suspicious activity.
CDD – Customer Due Diligence
The process of verifying customer identities and assessing risk profiles as part of AML obligations.
CFT – Counter Financing of Terrorism
Laws, regulations, and measures designed to detect and prevent the financing of terrorist activities.
CFPB – Consumer Financial Protection Bureau
A US regulatory agency overseeing consumer protection in the financial sector, including fintech compliance obligations.
DFPI – California Department of Financial Protection and Innovation
California’s state financial regulator oversees a broad range of financial services, including state-chartered banks, credit unions, money transmitters, fintechs, and consumer financial products. DFPI enforces compliance with California’s financial laws and the federal Bank Secrecy Act (BSA) where applicable, and conducts examinations and enforcement actions to ensure AML and consumer protection standards are met.
FCC – Financial Crime Compliance
The systems and processes financial institutions implement to comply with AML, CFT, sanctions, and other financial crime regulations.
FATF – Financial Action Task Force
An intergovernmental body that sets international standards for AML/CFT and counter-proliferation financing.
FinCEN – Financial Crimes Enforcement Network
A bureau of the US Treasury responsible for collecting and analyzing financial transaction data to combat financial crimes.
FFIEC – Federal Financial Institutions Examination Council
A US interagency body that prescribes uniform principles, standards, and guidelines for financial institutions, including AML program requirements.
MSA – Monitoring and Surveillance Activities
Processes and tools used to detect suspicious activity and financial crime patterns.
NYDFS – New York Department of Financial Services
New York State’s primary financial regulator, responsible for supervising and regulating the activities of state – chartered banks, licensed lenders, insurance companies, virtual currency businesses, and money transmitters. NYDFS enforces compliance with New York’s banking and financial laws, including anti–money laundering (AML) and cybersecurity regulations, and is known for high-profile enforcement actions in both traditional finance and fintech sectors.
OCC – Office of the Comptroller of the Currency
A US federal agency that regulates and supervises national banks and federal savings associations.
SAR – Suspicious Activity Report
A report filed with FinCEN when a financial institution detects activity that may involve money laundering or other financial crimes.
SEPA – Single Euro Payments Area
A European Union initiative that harmonizes euro-denominated bank transfers across participating countries.
STR – Suspicious Transaction Report
A report to financial intelligence units (outside the US) of potentially suspicious financial transactions.
SWIFT – Society for Worldwide Interbank Financial Telecommunication
A global messaging network used by financial institutions to securely transmit information and instructions through a standardized system of codes.
Sources:
- Reuters, January 2025
- The Paypers, July 2025
- Department of Justice, February 2025
- IT Spending on Risk Management in Banks Report, Celent Research, 2024 Edition.
- Guidance for a Risk-Based Approach for the Banking Sector, Financial Action Task Force (FATF), 2014.
- Statement on Effective Monitoring, The Wolfsberg Group 2024
- Accenture Risk Study 2024
- Applying AI to drive superior customer outcomes, Deloitte AI Institute, 2024.
- World Retail Banking Report (WRBR), Capgemini and Efma, 2022.