Turn transaction data into fraud AI
- with audit evidence
So Privacy, Model Risk, and TPRM can approve training, evaluation, and cross-institution collaboration - without raw data leaving your control.
The Deadlock
AI initiatives in financial services stall not on engineering - but on governance.
Production data blocked
Why: Policies often restrict production data use for ML due to PII exposure, purpose limitation, and residency constraints.
Consequence: Models trained on masked samples drift; fraud exposure increases.
MRM review cycles stall
Why: MRM and Privacy have no measurable evidence to evaluate - just "trust us."
Consequence: Every dataset requires bespoke approval; refresh cycles measured in quarters.
Cloud AI blocked by TPRM
Why: Sending data to vendor clouds triggers extended procurement review.
Consequence: Teams stay manual - and fall behind faster model refresh cycles.
Realm breaks this cycle - producing the artifacts that let each stakeholder sign off.
What You Get
Every Evidence Sprint delivers four concrete artifacts - not promises.
Privacy-Transformed Dataset
Model-ready data for training & evaluation - preserving fraud signals, governed and auditable.
Privacy / Utility Metrics
Quantified trade-offs MRM can evaluate - not "we anonymized it."
Attack Validation Results
Membership inference, cluster linkage (CVPL), re-identification attacks - proactive evidence before reviewers test it.
PAMOLA Evidence Pack v0
Config + lineage + metrics + compliance mapping - structured for DPO and audit review.
Evidence Pack v0
- Data-flow summary
- Residency statement
- Control mapping outline
- Run metadata
- Approvals trail (structure)
Privacy / Utility Metrics
- Privacy risk indicators
- Utility degradation measures
- Technique comparisons
- Policy-aligned risk gates (configurable)
Attack Validation
- Attack test plan
- Results summary
- Risk gate outcomes
- Remediation guidance
Artifact structure is standardized and review-friendly. Ask for a redacted sample during the demo.
Designed for repeatable approvals - the same evidence structure across refresh cycles.
Use Cases
Where Realm unlocks blocked AI initiatives in financial services.
Fraud Model Training on Production Data
Scenario:
Fraud teams can't use production transaction data for model retraining. Stuck with masked samples or aged snapshots.
What PAMOLA does:
Transforms transaction data inside your VPC with measured privacy. Attack validation proves it's safe.
Evidence delivered:
Transformed dataset + privacy/utility metrics + Evidence Pack with validation results for sign-off.
Who signs off: MRM + Privacy/DPO
AYITA expansion: Governed fraud investigation copilot with decision trace.
Scam Detection on Customer Communications
Scenario:
APP fraud signals live in chat logs, support tickets, investigator notes - but unstructured comms data is blocked.
What PAMOLA does:
Transforms unstructured text while preserving fraud signals. Simple redaction often destroys modeling signal - PAMOLA preserves utility and proves residual risk.
Evidence delivered:
Governed comms dataset + privacy/utility metrics + validation results + review-ready documentation.
Who signs off: Privacy/DPO + Legal
AYITA expansion: Investigator copilot for case summarization with a recomputable decision trace.
Cross-Institution Fraud Signal Collaboration
Scenario:
Banks want to share fraud signals and AML typologies, but raw data sharing is prohibited. Cloud clean rooms trigger TPRM concerns.
What PAMOLA does:
Transforms data inside each institution's perimeter. Each party gets its own Evidence Pack for independent approval.
Evidence delivered:
Per-party Evidence Packs + privacy/utility metrics + validation summaries + cross-org data flow map.
Who signs off: Privacy/DPO + Legal (often with TPRM/Security)
AYITA expansion: Cross-org investigation copilot with decision trace.
How It Works
From scoping call to governance approval in 30–45 days.
Scope
Define use case, data, success criteria. Deploy PAMOLA in your VPC. Align stakeholders.
Week 1–2
Evidence Sprint
Privacy transformations, metrics, attack simulation, Evidence Pack assembly.
Week 3–6
Deploy & Expand
Approvals complete. Production deployment. Expand to AYITA for governed intelligence.
Ongoing
Works in your environment: VPC / on-prem / controlled cloud. No data egress required.
Regulatory Alignment Support
Maps evidence artifacts to common regulatory and procurement expectations - helping teams prepare for internal governance and external reviews.
DPIA-style documentation
Designed to support DPIA-style assessments and AI governance documentation workflows.
Model Risk Management
Transformation methodology, attack validation, and lineage mapped to common MRM expectations.
TPRM / Vendor Risk
Security memo and deployment documentation addressing third-party risk concerns.
Cross-org Governance
Per-party Evidence Packs enable independent approval for consortium initiatives.
Ready to unblock your AI initiative?
30–45 day Evidence Sprint. Inside your perimeter. Audit-ready artifacts for MRM, Privacy, and TPRM sign-off.