DISCOVER AI
Financial Crime Intelligence
QMSLifecycle: Assure · ImproveEARLY_ACCESS

Quality Management System

Financial-Crime Investigation Quality Management

Measure investigation quality without confusing prediction with judgment.

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

Why Financial Crime Teams Require QMS

QMS brings statistical rigor and audit defensibility to AML and fraud quality assurance. By automating review sampling, consistency checks between narrative and evidence, defect taxonomy tracking, and reviewer calibration, QMS ensures that investigative conclusions withstand regulatory scrutiny.

Quantitative focus: Objective Defect Rates, Reviewer Calibration & Audit Defensibility
Regulatory alignment: OCC 2011-12 & Federal Reserve SR 11-7
Primary User Roles:
QA/QC DirectorsCompliance Risk OfficersInternal Audit Teams
Governed Risk Domains:
Quality Assurance (QA)Quality Control (QC)Regulatory Audit ReadinessReviewer Calibration
Repository Verified

Verified Technical Capabilities

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

Stratified & Risk-Based Review Sampling

VERIFIED

Automate case sampling across high-risk typologies, newly onboarded investigators, and edge-case dispositions to optimize QA coverage.

Evidence: Working/predictive_qc_v1_merged / corpus/sampler.py

Evidence-to-Conclusion Consistency Verification

VERIFIED

Verify whether documented findings in the narrative are supported by attached transaction receipts, KYC docs, and negative news.

Evidence: Working/predictive_qc_v1_merged / contracts/consistency_checker.py

Standardized Defect Taxonomy & Severity Scoring

VERIFIED

Categorize errors using standardized taxonomies (Technical Defect, Omission, Inadequate Investigation, Procedural) with clear severity weights.

Evidence: Working/predictive_qc_v1_merged / governance/defect_taxonomy.json

Reviewer Calibration & Inter-Rater Reliability

VERIFIED

Measure Cohen’s Kappa and inter-rater agreement across QA reviewers using blind multi-review test cases.

Evidence: Working/predictive_qc_v1_merged / eval/calibration.py
Operational Execution

Standard Operating Workflow

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

01

Stratified Sampling Selection

Ingest closed cases from CMS and apply policy-defined sampling criteria (random, risk-weighted, investigator-tenure weighted).

Inputs / Outputs:
In: Disposed cases from CMS or external case managers
Out: Active QA review queues with clear target dates
Human Decision Role:
QA Manager adjusts sampling parameters per quarterly plan
02

Calibrated Checklist Evaluation

Independent QA reviewers assess case files using standardized, objective question trees and evidence validation rules.

Inputs / Outputs:
In: Case file and standardized checklist rubric
Out: Scored review workpapers with specific defect citations
Human Decision Role:
QA Reviewer executes independent assessment
03

Rebuttal & Disagreement Resolution

Facilitate a structured review loop where primary investigators can view findings and submit formal rebuttals with supervisor oversight.

Inputs / Outputs:
In: Defect notification and primary investigator response
Out: Arbitrated quality determination and agreed coaching actions
Human Decision Role:
Operations supervisor arbitrates disputed findings
04

Continuous Feedback & Root-Cause Analytics

Aggregate defect patterns to identify training needs, scenario tuning requirements, or systemic data gaps.

Inputs / Outputs:
In: Aggregated defect data across all teams
Out: Quarterly QA dashboard and remediation recommendations
Human Decision Role:
Head of Compliance reviews systemic quality metrics
Interactive Preview

QMS 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 immutable, point-in-time snapshots of the case exactly as the investigator reviewed it
  • Prevents circular reasoning: QA reviewers cannot edit original case records or modify primary filings
  • Provides empirical inter-rater reliability scores to satisfy external bank regulatory examinations
  • Separates administrative oversights from substantive investigative deficiencies

Appropriate Use & Boundaries

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

  • •Focuses on evaluating investigative thoroughness and adherence to procedure; does not override bank policy determinations
Frequently Asked Questions

Technical & Compliance Inquiries

Most case managers treat QA as a simple checkbox field or basic second-review status. QMS is an independent, second-line quality governance platform with statistical sampling, objective defect taxonomies, inter-rater reliability calibration, and root-cause feedback loops.

Interoperable Suite Modules

CMSCase Management SystemTBTTrigger-Based TuningHRS LabHigh-Risk Scoring LabNLP LabNarrative Analytics & NLP Lab

Evaluate QMS in a Dedicated Synthetic Sandbox

Schedule an institutional technical review. We demonstrate Quality Management System using synthetic data formatted to your exact core schemas.