DISCOVER AI
Financial Crime Intelligence
Targeted Risk Solutions

Institutional Financial Crime Solutions

Purpose-built solutions addressing the highest-scrutiny compliance bottlenecks: high false-positive alert noise, model risk validation delays, manual case backlogs, and QA inconsistencies.

SOLUTION 01

AML Transaction Monitoring Modernization

Move from rigid, high-noise legacy rules to an evidence-backed detection and investigation ecosystem.

Financial crime teams are overwhelmed by 95%+ false-positive alert rates generated by outdated rules engines. Discover AI modernizes detection by combining empirical threshold tuning, explainable ML risk scoring, and unified case investigation.

The Core Industry Problem:

High operational costs, investigator alert fatigue, and delayed turnaround times on high-risk transaction reviews.

How Discover AI Solves It:

Data Lab harmonizes core feeds; TBT scientifically calibrates existing rules; HRS Lab scores transactions with transparent tree-SHAP factors; CMS provides investigators with instant customer context.

Measurable Institutional Outcomes:
Significant reduction in non-productive alert queues without sacrificing risk coverage
Defensible model risk workpapers aligned with OCC 2011-12 and SR 11-7
Accelerated investigator time-to-decision via contextual transaction timelines
Governance: Complete auditability of why rules were modified, with documented risk coverage retention and zero lookahead leakage in ML models.
SOLUTION 02

Defensible Rule & Scenario Optimization

Replace subjective threshold guessing with empirical sensitivity modeling and out-of-time backtesting.

Tuning monitoring scenarios in regulated banks requires defensible statistical evidence. Discover AI TBT models threshold elasticity, conducts out-of-time backtesting, and forecasts team capacity impacts before parameters are modified.

The Core Industry Problem:

Examiners scrutinize arbitrary threshold modifications; teams lack statistical tooling to prove that suppressed alerts were truly non-productive.

How Discover AI Solves It:

TBT runs continuous parameter sweeps against historical disposition data, generating comparative champion/challenger matrices and OCC-compliant workpapers.

Measurable Institutional Outcomes:
Elimination of undocumented rule parameter changes across AML and fraud systems
Accurate forecasting of investigator caseloads and operational capacity
Direct integration of QA defect findings into future tuning iterations
Governance: Provides an immutable, repeatable tuning framework that satisfies internal audit and regulatory scrutiny.
SOLUTION 03

Model Risk Management & Validation (SR 11-7)

Develop, validate, and monitor financial crime ML models under strict regulatory governance.

Bank model risk management (MRM) teams rightly reject black-box models that cannot demonstrate conceptual soundness or temporal stability. Discover AI builds explainable, monotonic models with automated drift telemetry.

The Core Industry Problem:

Model validation bottlenecks where high-performing models languish for quarters due to insufficient explainability or leakage risks.

How Discover AI Solves It:

HRS Lab strictly enforces point-in-time feature extraction, entity-aware cross-validation, and tree-SHAP attribution, generating audit-ready Model Cards.

Measurable Institutional Outcomes:
Zero temporal leakage guaranteed through bi-temporal data foundations
Monotonic constraints prevent nonsensical risk factor inversions
Real-time PSI/CSI population stability monitoring post-deployment
Governance: Decoupled model scoring from mandatory regulatory alert generation ensures compliance while supercharging investigator prioritization.
SOLUTION 04

Sanctions, Watchlist & Entity Resolution

Disambiguate matching names, screen complex corporate hierarchies, and uncover hidden networks.

Criminal networks deliberately obscure beneficial ownership behind layered corporate veils. Discover EntityGraph (DEG) delivers multi-script entity resolution and multi-hop graph traversal to expose concealed illicit relationships.

The Core Industry Problem:

False-positive alert backlogs from common name variations, multi-alphabet transliteration errors, and opaque shell company structures.

How Discover AI Solves It:

DEG performs phonetic, fuzzy, and address-clustering resolution, screening against versioned watchlists and mapping multi-hop corporate relationships.

Measurable Institutional Outcomes:
Multi-hop graph traversal up to 4 degrees of separation across corporate registries
Transparent match score decomposition eliminating subjective screening guesswork
Cryptographically logged watchlist provenance for regulatory audits
Governance: Transparent match component scores (name, DOB, nationality) ensure screening decisions are fully explainable to regulators.
SOLUTION 05

Second-Line Quality Assurance & Reviewer Calibration

Transform subjective investigation QA into an objective, statistically sound governance discipline.

Regulators frequently penalize financial institutions when second-line quality control fails to catch inadequate investigations or narrative discrepancies. QMS automates sampling, enforces objective defect taxonomies, and calibrates reviewer consistency.

The Core Industry Problem:

High grading variance between QA reviewers, inconsistent defect scoring, and lack of feedback loops back to detection engineering.

How Discover AI Solves It:

QMS deploys risk-stratified sampling, automated evidence-to-conclusion consistency checks, and Cohen’s Kappa inter-rater reliability calibration.

Measurable Institutional Outcomes:
Statistically calibrated QA reviews with measured inter-rater reliability
Early detection of unsupported narrative claims and missing investigative steps
Automated feedback loop channeling defect patterns into training and scenario tuning
Governance: Independent QA evaluations preserve point-in-time case evidence without modifying primary investigator records.