ThetaRay’s Three-Step Optimization Model for Transaction Monitoring 

U.S. financial institutions are facing unprecedented pressure. Regulatory scrutiny is intensifying, compliance costs are climbing, and increasingly sophisticated financial crime is outpacing the capabilities of legacy detection tools. 

Cognitive AI offers a smarter, lower-risk path forward. Instead of replacing existing infrastructure, banks can layer AI over current systems—unlocking rapid improvements in detection, reducing false positives, and surfacing transparent, regulator-aligned insights at scale. This approach empowers institutions to build trust in AI, enhance investigative workflows, and prepare for long-term system evolution—without the disruption of a full rip-and-replace.

For compliance teams navigating escalating demands with limited resources, Cognitive AI transforms AML from a bottleneck into a strategic advantage. With ThetaRay’s model, institutions gain a step-by-step path to stronger compliance, greater operational resilience, and future-ready architecture that evolves in step with shifting threats and supervisory expectations.

Step 1: Optimize & Enhance Existing Compliance Operations

(AI augmentation over the incumbent system)

• Overlay ThetaRay’s AI alongside your existing rules-based system—no need to rip and replace.

• Reduce false positives by up to 90%, cutting down investigation time and compliance costs.

• Prioritizes True Positive Alerts – intelligently filter and prioritize true positive alerts, focus on the most critical threats while reducing noise from false positives. 

• Provide explainable, risk-based alerts, enriching investigations with actionable intelligence.

• Improve SAR productivity by helping analysts focus on real threats.

Outcome: Immediate efficiency gains without disrupting current workflows.

Step 2: Parallel Testing & AI-Driven Optimization

(Replacing rule-based detection while running in parallel)

• Run ThetaRay’s AI-driven risk models in parallel with existing transaction monitoring rules.
• Gradually phase out legacy rules, reducing dependency on static logic.
• Monitor AI-generated alerts side-by-side with the incumbent system for performance validation.
• Fine-tune risk thresholds dynamically to align with regulatory expectations.
• Enhance regulatory confidence with explainable AI-driven insights.

Outcome: Demonstrate AI’s superior accuracy, lower operational costs, and improved detection capabilities—all while ensuring regulatory alignment.

Step 3: Final Option – A Full Conversion to Cognitive AI Core Detection

(ThetaRay becomes the bank’s primary transaction monitoring system)

• Decommission legacy rule-based monitoring, eliminating costly maintenance and manual tuning.

• Leverage ThetaRay’s Cognitive AI as the core detection engine for effective, dynamic financial crime detection.

• Maintain full transparency & auditability, ensuring compliance teams and regulators have clear visibility into risk coverage and risk mitigation decisions.

• Continuously improve detection accuracy through ongoing AI learning and refinement.

• Reduce total cost of ownership (TCO) by eliminating expensive professional services and system upkeep.

Outcome: A future-proof, AI-powered compliance system that scales with business growth, enhances efficiency, and delivers proactive financial crime detection.

The Smarter Route to AML Modernization

Rather than forcing a costly and disruptive rip-and-replace, ThetaRay’s three-step optimization model offers U.S. financial institutions a smarter, lower-risk journey toward compliance transformation. Starting with fast, low-friction AI overlay, institutions can immediately improve alert quality and reduce false positives—without disrupting existing workflows. As trust in AI grows, they can confidently scale its use through parallel testing and dynamic optimization, validating results and aligning with regulatory expectations.

Ultimately, this path enables a full transition to Cognitive AI at a pace that suits institutional needs—future-proofing compliance, reducing cost, and equipping teams to stay ahead of fast-moving threats. It’s not just a technological upgrade—it’s a strategic shift toward intelligent, explainable, and resilient financial crime detection.