CyberTRIZPEDIA

Transaction Monitoring Sensitivity vs False Positives

Replace static rule sets with adaptive behavioural analytics and feedback loops to sustain detection quality without overwhelming investigators.

CyberTRIZ analysis · Banking contradiction AML003 · one of 8,235 worked contradictions published by CyberTRIZ.AI

Regulations

FATFEU_AI_ACTNIST_AI_RMFBCBS_239

Business Context

Increasing monitoring sensitivity helps detect suspicious behaviour but also generates large volumes of false positives that consume investigator capacity.

Banking TRIZ Resolution

Monitoring should become adaptive rather than rule-heavy. Behavioural analytics, peer grouping, customer risk profiles, and model feedback loops improve alert quality while reducing unnecessary investigations.

Recommended Banking TRIZ Principles

Principle 15 - Dynamics

Principle 23 - Feedback

Principle 28 - Replacement of Mechanical Systems

Principle 35 - Parameter Changes

Expected Outcome

Fewer false positives

Better suspicious activity detection

Lower investigation workload

Improved AML effectiveness

TRIZ principles applied

P15 DynamicsP23 FeedbackP28 Replacement of Mechanical SystemsP35 Parameter Changes

Controls that address this (13)

FATF-AML-001 - Implement Risk-Based AML/CFT Approachoperational · critical priority · Implement a risk-based approach to AML/CFT covering: national risk assessment awareness, institutional risk assessment, risk-based allocatioFATF-CDD-001 - Implement FATF-Standard Customer Due Diligenceoperational · critical priority · Apply CDD measures to all customers: identify and verify customer identity, identify beneficial owners, understand the purpose of the busineFATF-R1-001 - Institutional AML/CFT Risk Assessmentgovernance · critical priority · Conduct a comprehensive institutional AML/CFT risk assessment covering: products and services, customer types, delivery channels, and geogra