DISCOVER AI
Financial Crime Intelligence
TBTLifecycle: OptimizeEARLY_ACCESS

Trigger-Based Tuning

Scenario, Threshold, and Trigger Tuning

Optimize monitoring scenarios without losing control of risk.

Dedicated Sandbox Target:https://tbt.discoveraisolution.com
The Operational Challenge

Why Financial Crime Teams Require TBT

TBT eliminates arbitrary rule threshold guessing in AML and fraud detection systems. By coupling historical alert efficacy data with out-of-time validation and operational capacity modeling, TBT provides financial crime teams with defensible scenario optimization, champion/challenger comparison, and full regulatory audit trails.

Quantitative focus: Defensible Threshold Sensitivity & Capacity Planning
Regulatory alignment: OCC 2011-12 & Federal Reserve SR 11-7
Primary User Roles:
Detection EngineersCompliance OfficersFIU Operations LeadersModel Risk Validators
Governed Risk Domains:
AML Transaction MonitoringFraud DetectionRule Governance
Repository Verified

Verified Technical Capabilities

Every capability below is backed by working code, verified algorithms, and evidence sources in the engineering repository.

Threshold Sensitivity Modeling

VERIFIED

Simulate alert volume, conversion rate, and productive hit rate shifts across continuous and discrete parameter bands.

Evidence: TBT Material / MADI_TBT_Framework.docx

Out-of-Time Back-Testing

VERIFIED

Test proposed thresholds against historical customer populations across past quarters to prevent temporal overfitting.

Evidence: TBT Material / Trigger-based Tuning Assessment Rerun.xlsx

Capacity & Workload Forecasting

VERIFIED

Forecast investigator FTE demand and SLA impacts directly from proposed parameter changes.

Evidence: TBT Material / MADI_TBT_Num_WT.pptx

Defensible Audit Workpapers

VERIFIED

Automatically generate versioned model-risk governance workpapers suitable for internal audit and regulatory review.

Evidence: TBT Material / CMR_TunedRules_2024.docx
Operational Execution

Standard Operating Workflow

How TBT executes within regulated banking environments, with explicit human oversight roles at every phase.

01

Scenario Intake & Ingestion

Ingest rule parameters, trigger definitions, and past 12–24 months of alert dispositions.

Inputs / Outputs:
In: Legacy rule configs and historical alert outcomes
Out: Normalized alert conversion baseline
Human Decision Role:
Detection engineer selects rule and observation window
02

Sensitivity Parameter Sweeps

Run automated multi-variable sweeps across velocity, amount, and lookback intervals.

Inputs / Outputs:
In: Boundary conditions and test step increments
Out: Volume sensitivity curves and productive hit probabilities
Human Decision Role:
Analyst inspects trade-off curve between volume and risk retention
03

Champion / Challenger Evaluation

Evaluate side-by-side performance of current baseline vs. candidate tuned configurations.

Inputs / Outputs:
In: Out-of-time validation slice
Out: Comparative yield, false-positive reduction, and risk coverage matrix
Human Decision Role:
Compliance manager approves candidate for staging
04

Audit Package Generation

Produce complete audit and governance documentation detailing rationale, exclusions, and test results.

Inputs / Outputs:
In: Final parameter selection
Out: Immutable tuning audit package for Model Risk Management
Human Decision Role:
Model Risk Validator signs off on production release
Interactive Preview

TBT Operational Exhibit

Synthetic demonstration illustrating data representation, factor decomposition, and audit trail generation.

CAS-2026-09418Apex Logistics & Freight LLC(CUST-883019)
SYNTHETIC DATA ILLUSTRATIONPending L2 Supervisor Disposition
Primary Typology
Rapid Movement of Funds
Total Trigger Volume
$482,500.00
Review Window
Trailing 14 Days
Risk Rating
HIGH RISK
Txn ID
Timestamp
Type
Counterparty & Corridor
Amount
TXN-9011
2026-09-02 09:14:22
Incoming Wire
Mariner Shipping Corp (Cyprus) CY
$240,000.00
TXN-9012
2026-09-02 11:32:05
Outgoing ACH
Vanguard Holdings Ltd US
$78,500.00
TXN-9013
2026-09-02 13:05:40
Outgoing Wire
Kestrel Trading International PA
$161,500.00
TXN-9024
2026-09-05 14:20:11
Incoming Wire
Mariner Shipping Corp (Cyprus) CY
$242,500.00
Discover AI Synthetic Demonstration EnvironmentSchema Source: DataLab / Synthetic Fixtures v2

Governance & Defensibility

  • Preserves full parameter change history and decision-time rationale
  • Excludes non-productive noise without suppressing emerging risk typologies
  • Produces audit-ready workpapers aligned with OCC 2011-12 and SR 11-7 expectations
  • Never equates lower alert volume with improved detection effectiveness

Appropriate Use & Boundaries

Per Charter Section 11, Discover AI transparently discloses operational limitations:

  • •Tuning outcomes depend on the historical accuracy of investigative dispositions in source systems
  • •Requires minimum historical volume to achieve statistical confidence in rare-event typologies
Frequently Asked Questions

Technical & Compliance Inquiries

No. TBT is an optimization and governance layer that sits on top of your existing systems. It consumes your rule configurations and disposition data, calculates optimal parameters, and generates the exact configuration files and audit documentation needed to update your engine.

Interoperable Suite Modules

Data LabGoverned Data EngineeringHRS LabHigh-Risk Scoring LabCMSCase Management System

Evaluate TBT in a Dedicated Synthetic Sandbox

Schedule an institutional technical review. We demonstrate Trigger-Based Tuning using synthetic data formatted to your exact core schemas.