ArcXA CCAR Solution

ARCXA - Comprehensive Capital Analysis and Review (CCAR)

"CCAR submissions are auditable only when you can trace every loan → every capital charge → every regulatory line item. Today that's manual and error-prone. ArcXA's semantic control plane (SCP) on IBM Power 10/11 makes that lineage automatic. You don't need to migrate to the cloud. Your existing Power infrastructure becomes audit-ready, and your stress-test reruns go from hours to minutes."

Acting as an intelligence connection layer, adds a mapping intelligence and data lineage layer that sits on top of legacy core banking infrastructure.

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Arcxa enhances today's CCAR Problem - manual, risky CCAR workflows:

Sample queries - 

1. Compliance analyst queries DB2: "Pull all commercial real estate loans originating Q1-Q4"
2. Data lands in Excel. Counterparty mappings checked by hand. Market data merged manually.
3. Stress-test model runs (Python, SAS, or proprietary code). Capital charges calculated.
4. Regulatory submission: "Why did Loan ABC get 8.5% capital charge vs. 7.2%?" → Audit trail is weak.
5. Fed audits filing. "Walk us through the logic." → 3 weeks of manual reconciliation.

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ArcXA Solution: Migration Readiness Assessment

CCAR requires banks to stress-test credit risk, demonstrate clear regulatory capital adequacy, and maintain tamper-evident data lineage for federal auditors.

Arcxa tames the CCAR Multi-System Problem

Today's setup:

• z/OS DB2 (mainframe) → Core loan origination, counterparties, master data
• IBM Power 10/11 DB2 → Risk ratings, collateral valuations, exposure calculations, stress-test parameters
• SAP HANA → Market feeds, regulatory consolidation, financials, CCAR reporting layer

The pain:

1. Loan data lives in z/OS. Risk data in Power. Market/reporting in HANA.
2. CCAR requires a single loan object with all data linked: origination + risk + market + regulatory mapping.
3. Manual reconciliation across systems = weeks of rework before Fed submission.
4. "Why did we bucket Loan_ABC differently this quarter?" → Requires tracing across three database schemas.
5. Stress-test reruns mean touching all three systems, waiting for ETL sync cycles.

Sample queries - 

1. Compliance analyst queries DB2: "Pull all commercial real estate loans originating Q1-Q4"
2. Data lands in Excel. Counterparty mappings checked by hand. Market data merged manually.
3. Stress-test model runs (Python, SAS, or proprietary code). Capital charges calculated.
4. Regulatory submission: "Why did Loan ABC get 8.5% capital charge vs. 7.2%?" → Audit trail is weak.
5. Fed audits filing. "Walk us through the logic." → 3 weeks of manual reconciliation.

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PowerGraph Proposes: Ai BANKING COMPLIANCE CCAR SYSTEMS;

Equitus ARCXA alongside KGNN (Knowledge Graph Neural Networks) on IBM Power10/Power11 (running z/OS or Linux with MMA matrix acceleration) creates a high-performance stack for accelerating CCAR (Comprehensive Capital Analysis and Review) compliance and loan portfolio oversight.

1. Equitus ARCXA: Semantic Mapping & Cryptographic Lineage

ARCXA serves as the mapping intelligence and data lineage layer sitting on top of legacy core banking infrastructure.

• Unified Loan Schema Mapping: ARCXA normalizes disparate loan data (e.g., commercial real estate, syndicated loans, retail mortgages) across multiple core systems into a single standardized CCAR ontology without requiring full ETL redesign.

• Tamper-Evident Audit Chains: Federal regulators require end-to-end data provenance for stress-test models. ARCXA generates a cryptographic audit chain at the rule and value level, verifying exactly how loan numbers were calculated and transformed.

• Zero-Trust Anomaly Detection: ARCXA flags null values, missing collateral disclosures, or schema anomalies before loan data reaches regulatory stress-testing pipelines.

2. Triple Store & KGNN: Deep Graph Risk Modeling

Rather than storing loan data in isolated tabular databases, a RDF Triple Store (Subject-Predicate-Object) structures credit exposure into a Knowledge Graph.

• Contextual Entity Linking: Connects borrowers, guarantors, ultimate beneficial owners (UBOs), collateral assets, and macroeconomic risk factors in a single interconnected graph.

• Graph Neural Network (KGNN) Reasoning: KGNNs run machine learning over the RDF graph to uncover complex, hidden risk patterns—such as contagion risk, concentration risk across interconnected subsidiaries, or hidden collateral re-hypothecation.

• Stress-Testing Simulation: During CCAR scenario runs (e.g., severe recession or interest rate spikes), KGNN propagates financial shock through graph edges to predict non-performing loan (NPL) default cascades.

3. IBM Power10/Power11 z/OS & MMA Hardware Engine

Equitus Arcxa runs on IBM’s enterprise ecosystem delivers the extreme compute and sub-second transaction throughput required for massive loan portfolios.

• MMA (Matrix Math Accelerator): IBM Power10 and Power11 processors feature built-in hardware acceleration specifically designed for dense matrix operations. This allows the graph neural network (KGNN) inference and deep-learning stress models to execute inline directly where the core banking data lives—eliminating slow network egress to external GPU clusters.

• Co-location on z/OS / IBM Power: Mainframe core banking environments host the live loan books. Executing ARCXA and KGNN within the same hardware boundary dramatically speeds up raw data ingestion and RDF triple creation for CCAR reporting cycles.

• Hardware-Level Encryption & Security: Supports the high confidentiality requirements for sensitive institutional borrower profiles and financial statements.

End-to-End CCAR Workflow Integration

Summary of Key Benefits

• Hours instead of weeks: Reduces CCAR aggregation and stress-test data assembly cycles significantly.

• Defensible compliance: Regulators can trace every metric back to its origin field with cryptographic proof via ARCXA.

• Systemic risk visibility: KGNN spots hidden leverage and concentration risk that traditional relational databases miss.

• In-situ execution: IBM Power MMA hardware handles deep learning calculations locally on mainframe-grade systems without moving sensitive bank data.

Arcxa - Comprehensive Capital Analysis and Review  for Banking CROs

"CCAR submissions are auditable only when you can trace every loan → every capital charge → every regulatory line item. Today that's manual and error-prone. ArcXA's semantic control plane on IBM Power 10/11 makes that lineage automatic. You don't need to migrate to the cloud. Your existing Power infrastructure becomes audit-ready, and your stress-test reruns go from hours to minutes."

 

 

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