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Build AI That Speaks Compliance

Your compliance artifacts, KYC documents, and risk policies contain the regulatory DNA of your institution. MergeOn transforms PCI/SOC reports into audit-ready datasets, parses KYC/AML checks with evidence trails, and extracts risk workflows into structured training data that teaches AI how to navigate financial regulation.

PCI DSS
SOC 1/2
KYC/AML
Basel III
GDPR
100K+
Controls Mapped
Every
Check Evidenced
87%
Faster Audits
Zero
Findings Missed
Compliance Data

PCI/SOC Artifacts → Audit-Ready Datasets

Transform compliance documentation into structured training data that teaches AI to understand controls, evidence requirements, and audit procedures

Compliance Artifact Processing Pipeline
Q4 2024 Audit Ready
Raw Compliance DocumentsUNSTRUCTURED
PCI DSS Self-Assessment v4.0
394 controls • 12 sections • PDF format
SOC 2 Type II Report
Trust service criteria • 247 pages
Network Security Policies
87 policies • Version controlled
Penetration Test Results
Q3 2024 • Critical findings: 0
Access Control Matrix
Excel • 2,847 permission mappings
Structured Training DataAI-READY
{ "control_id": "PCI-DSS-3.4.1", "requirement": "Render PAN unreadable using encryption", "implementation": { "method": "AES-256-GCM", "key_management": "HSM-backed", "rotation_period": "annual" }, "evidence": [ { "type": "configuration", "source": "crypto_policy_v3.pdf", "page": 47, "last_validated": "2024-10-15" }, { "type": "test_result", "source": "pentest_q3_2024.pdf", "finding": "compliant" } ], "compliance_status": "PASS", "risk_rating": "low", "next_review": "2025-01-15" }
Identity Verification

KYC/AML Parsing with Evidence Links

Extract verification checks, risk scores, and compliance decisions from KYC/AML documents with complete evidence trails for training compliant AI systems

Customer Onboarding
IDENTITY
Parse identity verification documents, extract data points, validate against sanctions lists, and create training examples with full audit trails.
1Document authenticity verified
2Biometric match confirmed
3Address proof validated
4Risk score calculated: Low
Sanctions Screening
AML
Extract entity data from documents, match against OFAC/UN/EU lists, document false positives, and build training sets for name matching AI.
1Name variations generated
2Fuzzy matching applied
3No matches found
4Clearance documented
Transaction Monitoring
PATTERN
Process transaction histories, identify patterns, flag anomalies, and create labeled training data for suspicious activity detection.
1Baseline behavior established
2Velocity checks passed
3Geographic consistency verified
4No structuring detected
PEP Screening
ENHANCED
Extract names and relationships from documents, check against PEP databases, document relationship networks, and train AI on risk assessment.
1Direct PEP check: Negative
2Family associations reviewed
3Business relationships mapped
4EDD not required
Source of Wealth
VERIFICATION
Parse financial statements, employment records, and business documents to verify wealth sources and create training data for risk models.
1Income documents analyzed
2Tax returns validated
3Business ownership confirmed
4Wealth source: Legitimate
Ongoing Monitoring
CONTINUOUS
Extract updates from news, regulatory changes, and transaction patterns to maintain current risk profiles and train adaptive AI models.
1Adverse media: Clear
2Regulatory status: Current
3Risk score: Unchanged
4Next review: 6 months
Risk Management

Capital, Credit & Ops Risk Workflows

Transform risk policies and procedures into structured training data that teaches AI to understand risk frameworks, limits, and escalation procedures

CA
Capital Risk
847 policies extracted
CR
Credit Risk
1,247 rules mapped
OP
Operational Risk
524 workflows parsed
Extracted Capital Risk Policies
TIER 1 CAPITAL RATIO
Maintain minimum Tier 1 capital ratio of 6% under Basel III. Breach triggers immediate CEO notification and trading restrictions.
Source: Capital_Policy_2024.pdfSection: 3.1.2View evidence
STRESS TESTING
Conduct quarterly stress tests with severely adverse scenarios. Capital buffer must withstand 400bps rate shock.
Source: CCAR_Framework.docxSection: 7.4View evidence
LEVERAGE RATIO
Supplementary leverage ratio must exceed 5% for GSIBs. Daily monitoring required with automated alerts at 5.5%.
Source: Risk_Appetite_Statement.pdfSection: 2.8View evidence
AI Applications

What You Can Build With This Data

Compliance-trained AI that understands financial regulation

AU

Audit Automation

Train AI to prepare for audits by mapping controls, gathering evidence, and identifying gaps. Built from thousands of successful audit reports.

→ 87% faster audit prep
CM

Compliance Monitoring

Build models that continuously monitor for regulatory changes and assess impact on existing controls. Trained on regulatory updates and implementations.

→ Real-time compliance status
RM

Risk Assessment

Develop AI that evaluates risk across capital, credit, and operational dimensions using your actual risk frameworks and historical decisions.

→ Consistent risk scoring
KY

KYC Intelligence

Create models that streamline customer onboarding while maintaining compliance. Trained on approved and rejected cases with full reasoning.

→ 70% faster onboarding
TM

Transaction Monitoring

Train AI to detect suspicious patterns using labeled transaction histories and investigation outcomes. Reduces false positives while catching real risks.

→ 65% fewer false positives
RR

Regulatory Reporting

Build systems that automatically compile regulatory reports from source data. Trained on successful submissions across multiple jurisdictions.

→ Zero filing errors
Real Results

Compliance Operations, Transformed

10K+
Controls Mapped
Across frameworks
87%
Faster Audits
Evidence ready
$2.7M
Saved Annually
Compliance costs
Zero
Regulatory Fines
Since deployment
100%
Evidence Traced
Full audit trail
24/7
Monitoring
Continuous compliance

Train AI That Understands Compliance

See how banks and fintechs are building regulatory AI with MergeOn-processed compliance data

See Compliance DemoReview Security Docs