Validating AI-Driven Transaction Monitoring
A customer needed to test next-generation AML models that could reduce the manual review burden of 3,000 monthly alerts - of which 95% were false positives - and deliver faster, more accurate risk detection.
Outcomes
70%
Reduction in Claims
10.5%
SAR Hit Rate
25%
Manual Review Time Saved
0%
Sensitive Data Exposure
Technology Vendors Suited to Evaluation
Business Problem
The bank’s existing transaction monitoring system was struggling to keep pace with modern financial crime patterns.
- Reducing efficiency: Over 95% of alerts were false positives, wasting time and resources on non-risk cases.
- Manual review overload: Analysts were reviewing around 3,000 alerts per month, with only 5% confirmed as genuine, creating bottlenecks and fatigue.
- Outdated rule-based logic: Legacy systems relied on static rules that couldn’t adapt to emerging fraud typologies or behavioural shifts, limiting their effectiveness and accuracy.
The result was a costly, slow, and reactive process – one that failed to prioritise genuine risk and drained investigative capacity.
Challenges
- High false positive rates: Over 95% of transaction alerts were incorrectly flagged, creating noise and inefficiency.
- Manual workload strain: Around 3,000 alerts required review each month, overwhelming compliance teams.
- Slow risk detection: Genuine suspicious activities were delayed by the volume of low-risk alerts.
- Rigid rule-based models: Legacy systems couldn’t adapt to evolving financial crime patterns or behaviours.
- Limited explainability: Existing tools lacked transparency, making it hard to justify or audit alert decisions.
From Idea to Evidence with NayaOne
A Tier 1 bank set out to modernise its transaction monitoring process by testing AI-driven models that could cut false positives and speed up investigations.
They focused on three key areas:
- Detection accuracy: Benchmarking AI models against legacy rule-based systems.
- Operational efficiency: Measuring reductions in manual workload and alert handling time.
- Governance and assurance: Ensuring safe, compliant testing using synthetic data inside a secure sandbox.
Through the NayaOne platform, vendors were deployed within an isolated, production-like sandbox powered by synthetic transaction data – enabling realistic testing without exposing customer information. Each AI model was evaluated for precision, adaptability, and explainability, using identical data and parameters to ensure comparability.
AML and fraud teams reviewed outputs through NayaOne’s structured scoring framework, capturing accuracy, prioritisation logic, and operational fit. Every test run was logged, audited, and consolidated into evidence packs – providing a single view of model performance, governance readiness, and return on investment.
Impact Metrics
PoC Timeline Reduction
6 weeks with NayaOne vs 12 – 18 months traditionally
Time Saved in Vendor Evaluation
10 - 16 months
Decision Quality
Decision quality improved from manual judgment under alert fatigue to data-driven, explainable risk decisions - faster, more consistent, and easier to defend.
KPIs
- False Positive Reduction – Measured decrease in inaccurate alerts, targeting a 70 – 80% drop versus baseline performance.
- Alert Prioritisation Accuracy – Validated precision of AI scoring models to ensure genuine threats surfaced faster.
- Review Efficiency – Tracked reduction in analyst review time per case and overall alert backlog.
- SAR Filing Speed – Measured time saved in escalating and reporting genuine suspicious activity (goal: 30 – 40% faster).
- Model Explainability – Evaluated clarity and traceability of AI-driven risk scoring to meet internal and regulatory standards
Validate your AML Models Before Deployment.
Test AI-driven transaction monitoring tools safely inside NayaOne’s secure sandbox — using synthetic data, real metrics, and structured evaluations to cut false positives and speed up risk detection.
