Why Rule-Based Transaction Monitoring Is No Longer Enough
Explore the limitations of traditional monitoring approaches and what comes next in detecting modern financial crime.
READ THE BLOG ↗
The Future of Financial Crime Detection
Book a discovery callPreparing for the future of financial crime detection
Financial crime is becoming harder to detect across real-time payments, cross-border transactions, and complex ecosystems.
Traditional monitoring systems are struggling to keep pace, generating high volumes of alerts while missing complex financial crime patterns.
Why financial institutions are rethinking detection?
- Increasing transaction volumes
- High false-positive alerts
- Investigators gathering data across systems
- Limited visibility into complex financial crime
- Limited visibility into complex financial crime
What you’ll learn?
- Why traditional AML monitoring struggles
- How financial crime is evolving across networks
- The limitations of rule-based detection
- How AI improves detection accuracy
- Ways to reduce investigation workload
WHITEPAPER
AI in AML: Closing the Gap Between Detection and Regulatory Expectations
How financial institutions can improve detection accuracy, reduce investigative burden, and meet rising regulatory expectations.
Download the Whitepaper ↗A more modern approach to financial crime detection
Leading institutions are moving beyond static rules toward behaviour-based approaches.
By analysing activity across transaction networks, they can identify hidden patterns, improve alert accuracy, and reduce investigation workloads.
Improve detection accuracy
Surface hidden patterns across transaction ecosystems
Reduce false positives
Help investigators focus on higher-value alerts
Strengthen investigations
Enable more consistent decision-making
Support regulatory readiness
Improve transparency and defensibility
Discuss Your Financial Crime Detection Challenges
If you’re navigating false positives, investigation inefficiencies, or increasing regulatory expectations, we’d be happy to share what we’re seeing across financial institutions.
- Discuss your current AML challenges
- Explore approaches to improving detection
- Gain insights from similar institutions
Case Study
How Shift4 Increased Productive Alerts by 70% with Cognitive AI Transaction Monitoring
See how Shift4 used ThetaRay’s Cognitive AI platform to reduce false positives, accelerate investigations, and improve AML monitoring performance across its European operations.
What you’ll find inside
- 80% reduction in total alert volume
- 70% increase in productive alerts
- 86% reduction in false positives
- Investigation time reduced from hours to minutes
Start preparing for the future of financial crime detection
So who are ThetaRay?
ThetaRay helps financial institutions detect complex financial crime across transaction networks using proprietary AI models.
Our approach improves detection accuracy, reduces false positives, and enables more consistent, defensible investigations aligned with regulatory expectations.
Ready to explore what this could look like for your organisation?
Ready to explore what this could look like for your organisation?