Best AML software for banks and payment companies, and what good looks like in 2025

Why AML software matters more than ever

Banks and financial institutions today are navigating an unprecedented mix of regulatory pressure, technological possibility, and operational fatigue.

Yet despite years of investment, the industry still struggles with effectiveness. According to The AML Megaminds Report, 70% of AML professionals believe current controls are inefficient, and Europol estimates that only 1% of laundered money is ever intercepted.

Across Europe, from the Nordics to the UK, banks and financial institutions are rethinking AML modernization strategies as regulations tighten and AI becomes essential.

This gap between effort and outcome underscores why banks are rebuilding their AML foundations.

Legacy systems and manual workflows can no longer keep pace with evolving risks, driving the shift toward AI-native, data-driven platforms that enable real-time detection and explainable automation.

Across recent discussions on Strise's The Laundry podcast, we spoke with experienced AML and compliance leaders from leading banks, fintechs, and advisory firms across Europe and the UK.

Despite different business models, one common theme emerged: the future of AML belongs to institutions that treat data as infrastructure, not a by-product.

This guide distils those practitioner insights into what “good” looks like, how leading teams are modernising their AML stack, and which vendors are setting the standard for trustworthy automation.

You’ll learn:

• How practitioners define effective AML modernisation.
  
• What pitfalls slow banks down.
  
• How AI, data quality, and explainability converge.
• A practical overview of leading AML platforms.

What top AML practitioners are saying

1. Data is both the problem and the solution

“We still see teams running onboarding and periodic reviews on spreadsheets — 80 columns, 150 rows. It makes me anxious just talking about it.” — Robert E. Bing, Partner, Davies Group

From large banks to fast-moving fintechs, the same pattern repeats: compliance teams are buried under data that’s duplicated, outdated, or siloed.

The best teams are reversing this. Instead of hoarding, they’re curating, building living, connected profiles that flow across onboarding, monitoring, and review cycles.

2. Perpetual KYB/C monitoring is replacing periodic reviews

“We put more emphasis on continuous relationship monitoring. You need to have AML data to verify that against.” — Heili Veskimeister, MRLO & Head of Compliance, Lightyear

Institutions are designing event-driven monitoring where every ownership change, sanction update, or new adverse-media signal automatically recalibrates risk scores.

This shift eliminates dead-weight reviews and gives analysts real-time context for decisions.

3. AI adoption and explainability in AML software

“You still need to understand the methodology. If you’re responsible for building controls, you must know how the tools work.” — Heili Veskimeister, Lightyear

Automation will handle document parsing, adverse-media searches, and false-positive clearing. Humans will supervise model design, validation, and risk logic.

The emphasis is shifting from “Can AI do it?” to “Can we explain why it did it?”

Also, according to Advisense’s 2025 AML State of Play: Special Edition on AI, poor data quality is one of the biggest blocker to effective AI adoption in financial crime compliance. AI models depend on complete, accurate, and structured data, yet most institutions still rely on fragmented legacy systems and manual spreadsheets that hinder automation and insight generation.

In our The Laundry episode on AI agents, the experts added that the next evolution will see humans governing AI, not competing with it.

4. Innovation and compliance can co-exist

“Compliance isn’t a showstopper anymore, it’s something we co-create.” — Karoline Tyan, Executive in AML/CFT, Avanza Bank

Banks like Avanza prove that product thinking and AML compliance can, and must, merge.

When compliance teams work as product owners, the outcome isn’t just control, it’s customer trust and scalable innovation.

“The real differentiator between two banks isn’t their products, it’s how fast and intelligently they onboard.” — Robert E. Bing, Partner, Davies Group

At Vipps MobilePay, Øverby calls this the “early-bird approach”, involving risk and compliance early in every product process so teams can design compliance into the business rather than layering it on later.

Payment companies face the same regulatory obligations as banks, but with faster onboarding cycles, cross-border merchants, and higher transaction volumes.

How payment companies automate AML for efficiency:

5. Culture and collaboration define AML maturity

“Each party, banks, asset managers, and payment companies, has data the others need. We should find safer ways to share it.” — Jaypee Soule, Head of Second-Line Compliance, PensionBee

Cross-institution data sharing remains primitive, but collaboration is now seen as the ultimate unlock for speed and trust.

Vipps MobilePay provides a case study in cultural design: open Slack channels where product and risk teams share memes, questions, and quick clarifications, making compliance part of everyday conversation, not a gatekeeping function.

“Having the right technology in place to react faster instead of implementing old systems that take forever, that’s what modern compliance looks like.” — Jaypee Soule, PensionBee

Top AML automation platforms in 2025

Note: The following overview is not an endorsement or recommendation from any guest featured on The Laundry podcast. It is an independent, curated view of AI tools for financial crime prevention showing strong traction in the banking sector.

Each supports modern, AI-enhanced compliance operations, from onboarding to perpetual monitoring. Many leading AML platforms now automate UBO discovery and verification, helping banks maintain transparency across complex corporate ownership structures.

Data providers

• Sayari – Provides commercial risk intelligence with deep visibility into ownership structures, trade networks, and cross-border relationships.
• Kyckr – Supplies verified, direct-from-registry company data to support reliable KYC and KYB checks.
• LexisNexis – Delivers sanctions, PEP, and adverse media screening data, including extensive historical coverage.
• Dow Jones Risk & Compliance – Offers premium datasets for sanctions, PEPs, and adverse media, trusted by global financial institutions.

AI tools for AML compliance

• Strise – AML automation built on trusted data. Fuses registry, watchlists, and media sources into explainable risk profiles. Used by leading banks, financial institutions, and payment companies such as PwC, Nordea, and Corpay.
• Napier AI – AI-powered AML compliance platform built for scale, combining transaction monitoring, sanctions screening, and adaptive analytics.
• Lucinity – AI-driven case management platform that applies behavioral analytics to help analysts investigate and resolve alerts faster.
• Greenlite AI – Deploys AI agents that automate screening, due diligence, and alert handling, reducing repetitive analyst work.

Case management and lifecycle tools (CLM)

• Fenergo – ‍Enterprise CLM platform covering onboarding through ongoing reviews, with built-in regulatory workflows.‍
• Pega – ‍Unified case & alert management platform that consolidates AML, fraud, sanctions, and transaction alerts into a single investigative framework.
• KYCPortal – A highly configurable client lifecycle management solution that centralises due diligence, risk scoring, and customer outreach.
• KYC Hub – A cloud-native compliance automation platform combining onboarding, AML screening, ongoing monitoring, and case management.

Final thought

“Compliance can be a competitive advantage when it’s built on curiosity, not control.” — Marit Rødevand, CEO, Strise

The next generation of AML teams won’t just meet regulations, they’ll compete on trust, speed, and explainability.

The winners will be the institutions that start with data, apply explainable AI, and design compliance as a product, not a constraint.