Tieto Banktech

AI-powered AML transaction monitoring for European banks

Q: Do we have to replace our current monitoring system?

No. AML Explore layers onto your existing AML stack and proves the lift on your data first.

Q: What does the pre-analysis actually show?

Your own labelled, anonymised alerts, run through Explore, so you see the real false-positive reduction and efficiency gain for your operations.

Q: Is the AI explainable for regulators and auditors?

Yes. Every score comes with documented reasoning on transaction and customer level, built for regulated banking and evidence-based supervision by the EU's AMLA, the FCA and FINMA.

Q: How does it help against AI-enabled crime?

It learns behaviour and reveals networks, so it can catch mules across different gangs who never transact directly, which static rules miss.

Q: How fast can we see the results?

The pre-analysis runs on data you already have, so you see real numbers without a full deployment project.

Q: What about instant payments?

Explore is built for real-time European rails, so scoring and investigation keep pace with SEPA Instant rather than sitting in a queue.

AML Explore scores every alert, explains exactly why it flagged, shows the network behind it, and helps your team close and report cases faster. On your own data, on top of the stack you already run.

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Tieto’s proven financial crime defence

European financial institutions connected

130+

Detection rate maintained over 25 years

~90%

Fraud monitoring and support

24/7

The 2026 squeeze

The rules have changed. Across Europe and the UK, regulators now expect you to explain every alert and defend every decision. From 2027, the EU brings a single rulebook and a data-driven supervisor, the FCA and FINMA focus on outcomes. Instant payments give you seconds to act as AI-powered criminals and mule networks outpace static rules. Yet 85–95% of alerts are still false positives, consuming analysts’ time on clean transactions.

Modern fraud

Why traditional AML monitoring struggles

Static rules and high alert volumes make it harder for AML teams to identify genuine risk. AML Explore adds AI-supported scoring, explainability and network analysis to existing monitoring, helping investigators focus on the cases that matter.

1

False positives consume analyst time

When most alerts lead nowhere, investigators spend valuable time clearing noise instead of focusing on higher-risk cases.
2

Hidden networks evade static rules

Money laundering can move through connected accounts and mule networks that individual transaction rules struggle to identify.
3

Decisions need to be explainable

A risk score alone is not enough. Investigators need to understand and document why activity was flagged and what evidence supports the decision.

No guessing. Just data.

Validated on your data, not a vendor demo

Most vendors show you their benchmarks. We show you your numbers. A short pre-analysis of your labelled, anonymized data shows how AML Explore can reduce false positives and improve efficiency — before you deploy anything. Proof, not a pitch.

AML Explore capabilities & benefits

Score it. Explain it. See the network. Close it faster.

Prioritize risk, understand transaction networks and give investigators the context they need to reach defensible decisions faster.

Reduce AML false positives and focus on real risk

The problem: Your team opens the day to hundreds of alerts, knowing most are noise. But every one still needs attention because the real risk is hiding somewhere in the queue.

How AML Explore helps: AML Explore learns from historical outcomes and scores risk, helping investigators prioritise the alerts that actually matter.

The outcome: Less time clearing false positives. More time investigating genuine risk.

Detect money laundering patterns rules can miss

The problem: Money moves through mule networks, across accounts and borders. Static rules can see individual transactions but struggle to see the bigger pattern.

How AML Explore helps: Behavioural clustering and network views reveal relationship clusters and hidden patterns that transaction-level rules may miss.

The outcome: Detect coordinated laundering and mule activity while it is still moving — not in the post-mortem.

Investigate and close AML cases faster

The problem: Once you understand the case, you still have to gather the evidence, document the decision and build an audit trail. The write-up can take longer than the investigation itself.

How AML Explore helps: It brings context into the investigation and helps analysts move from alert to case closure and reporting faster.

The outcome: Less time gathering and documenting. More time investigating genuine risk.

Make every AML decision explainable

The problem: An alert fires, but the system doesn't tell you why. When a reviewer or regulator asks, the analyst has to reconstruct reasoning the system never showed them.

How AML Explore helps: Explainable AI shows the patterns and evidence behind the alert, giving investigators a clear rationale for why it was flagged.

The outcome: Explainable, auditable decisions your team can defend.

European rails, European rules, European patterns

The problem: European banks operate under distinct payment rails and an evolving regulatory framework, from SEPA Instant and PSD2 to AMLR and AMLA supervision.

How AML Explore helps: Built for the European environment, AML Explore aligns monitoring with how transactions move and how AML decisions are examined.

The outcome: AML monitoring built for Europe, not adapted to it.

Modernize AML without ripping out what already works

The problem: Adding new AML technology can mean a major replacement project, with the cost, integration effort and disruption that comes with it.

How AML Explore helps: AML Explore is designed as an AI layer for transaction monitoring, extending the existing monitoring environment rather than positioning AI as a complete replacement.

The outcome: Add smarter AML capabilities while building on the investment you already have.

Prove the value on your own data before you commit

The problem: Vendor benchmarks show how a solution performed on someone else's data. They don't tell you what it will do with your customers, transactions and alerts.

How AML Explore helps: AML Explore can be evaluated on the bank's own labelled data, so performance is demonstrated against the bank's actual environment rather than a vendor-selected scenario.

The outcome: Your data. Your results. Proof before commitment.

AML Explore demo

See AML Explore in action

See how AML Explore brings AI scoring, transaction networks, explainability and investigation support together in one workflow — helping analysts move from an alert to a clearer understanding of risk.

AML Explore demo

Customer transaction network demo - find the risk

The case: a round-tripping scheme

Edward Sinclair, a building contractor, quietly controls Northfell Estates Ltd — a property shell held by an associate. Cash from undeclared work enters Northfell as small, structured deposits, always under the reporting threshold (placement).

Edward then pays Northfell €76,400 against fictitious renovation invoices. Northfell forwards €56,100 to strawman Marcus Reid as “project management fees”; Marcus parks €49,700 in Weybridge Capital Ltd (layering); and Weybridge lends €43,250 back to Edward as a private loan (integration) — the money returns clean, about 10% thinner per hop.

On the side, Marcus skims structured cash and wires €11,900 abroad via Mayfair FX Bureau to an unknown beneficiary. A conventional rules engine catches only one thing here: the structured cash deposits into Northfell (amber) — every other payment passes the rules. Toggle Risk color to watch the Atlas model isolate the cycle.

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Modern AML needs modern defense

Most AML alerts lead nowhere – wasting valuable analyst time. In this video, Clarance Therstam from Tieto Banktech explains how AI can enhance existing transaction monitoring, learn from your data and help teams focus on the alerts that truly matter.

Before the rulebook, not after

The supervisor will ask you to defend the decision. Have the evidence.

Run AML Explore on your real alerts and see the false-positive reduction, the explained decisions, and the networks for yourself. It layers on, so there is nothing to rip out, and it gives you rationale you can put in front of a regulator.

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