For many years financial institutions have invested heavily in tools that detect suspicious activity. However, detection is only one part of the equation, and another real challenge lies in turning alerts, data and risk signals into fast, consistent decisions. The Global State of RegTech 2026 report - authored by RegTech Analyst and Parker & Lawrence Research - took the time to examine the shifting trends in financial crime stacks and how companies must respond.

As part of the research for the report, Parker & Lawrence Research interviewed market leaders in the space on how they are transforming financial crime stacks for the challenges of today and what comes next.

On this occasion, the firm spoke with Gion-Andri Büsser and Sebastian Hetzler, co-CEO's of IMTF, which is a provider of Siron®One, a financial crime compliance platform covering transaction monitoring, sanctions screening, KYC/CDD, fraud and case management. Parker & Lawrence Research detailed that IMTF's strength is its balance of depth and stability.

This interview was part of the wider Global State of RegTech report conducted by RegTech Analyst and Parker Lawrence Research. To download the full report, click here.

Transaction monitoring is no longer a standalone discipline. Across the industry, the traditional model, scenarios trigger alerts, analysts review them, cases are escalated, is under mounting strain. The conditions that once made siloed compliance controls workable have fundamentally shifted, and the institutions still operating that way are increasingly exposed.

Instant payments have compressed the time available for detection and intervention. Cross-border activity is generating more complex data trails. Financial crime typologies are growing more networked, with fraud, sanctions, know your customer (KYC) and anti-money laundering (AML) signals overlapping more than ever. Meanwhile, regulators expect firms not just to monitor, but to demonstrate that their monitoring is effective, governed and explainable.

The Global State of RegTech 2026, published by Parker & Lawrence, captures the direction of travel clearly. The industry is shifting towards entity-centric, cross-domain financial crime decisioning. Alerts, customer lifecycle data, transaction behaviour and external intelligence all need to be pulled into a unified risk view. Institutions need to assess customers and networks holistically, not treat every alert as an isolated event.

IMTF's Siron®One platform has been built with precisely this connected architecture in mind.

IMTF VP product management Youness Bouchabchoub said, 'We have the two layers in our platform, the detection layer and the decision layer.'

Many financial institutions are still running fragmented financial crime stacks. Transaction monitoring, screening, KYC, fraud and case management frequently sit in separate tools, each with its own data model, workflow and reporting infrastructure. Integration is difficult, and the same customer may be assessed differently depending on which domain is looking at them.

The operational consequences are significant. Analysts face high volumes of false positives. Investigations become repetitive and context-poor. Governance teams struggle to test and tune models consistently. Senior leaders lack a clear view of risk exposure across customers, products and jurisdictions.

Legacy systems deepen the problem. They are expensive to maintain, slow to adapt, and at large institutions may be deeply embedded in core infrastructure. For mid-tier firms, the priority is faster deployment and lower complexity, without sacrificing regulatory confidence.

The core challenge is not alert generation. It is connecting detection with decisioning, explaining why certain alerts are prioritised, and building a framework for continuous improvement.

What Siron®One does differently

Siron®One is a unified financial crime compliance platform that brings together customer onboarding and KYC, transaction monitoring, sanctions screening, fraud detection, alert and case management, and AI-powered decision support into a single operating environment.

At its foundation is a consolidated data layer. Siron®One draws in data from onboarding, KYC, transactions and external intelligence sources into a consistent structure, connecting with core banking, payment systems and third-party providers via open, API-based architecture. This enables a more complete customer risk view, with onboarding and KYC data directly informing transaction monitoring scenarios - creating contextual, risk-based detection rather than threshold-driven noise.

The platform's transaction monitoring is configurable by customer category, allowing firms to define different thresholds and risk-based logic across segments without writing code. A threshold appropriate for a private individual is unlikely to be suitable for a corporate client or a construction business; Siron®One allows institutions to tailor their detection logic accordingly.

Governance capability is built into the product. Siron®One supports both forward and backward simulation, meaning firms can test scenario changes against production data before deployment. This matters because monitoring changes affect alert volumes, analyst workload and risk coverage. The ability to model impact ahead of rollout supports more controlled, auditable change management.

The decision layer ties it together. Analysts can review transaction monitoring alerts alongside related screening, KYC, fraud and customer-risk signals in a single 360-degree view, rather than working across disconnected tools. Workflow support covers assignment, escalation, collaboration, documentation and suspicious activity reporting, with management dashboards providing visibility into operational performance, alert volumes and risk trends.

IMTF's approach to artificial intelligence is hybrid by design. Rules remain important for known typologies and regulatory transparency. AI is deployed where it improves prioritisation, anomaly detection and contextual insight - not as a replacement for human judgement.

Siron®One includes an alert predictive score that benchmarks new alerts against historically confirmed ones to support analyst prioritisation. It also features an entity deviation score, which uses clustering to flag customers behaving differently from their peer group. AI is additionally applied in name screening and entity resolution, with chatbot-style analyst support in development. Human review remains central to every decision.

Read the original post from Parker & Lawrence Research here.