Secure, Scalable
Financial Architecture.
Modernize legacy infrastructure with high-performance batch processing, real-time fraud detection algorithms, and rigorous FINRA/SOX compliance baked into the foundation.
Uncompromising FINRA & SOX Compliance
In the highly regulated financial sector, compliance is not an afterthought—it is the architecture. We build systems that enforce Sarbanes-Oxley (SOX) controls natively, ensuring data immutability, strict access governance, and comprehensive audit trails. Our FINRA-compliant data retention policies guarantee that your electronic records are securely archived and easily retrievable.
Immutable Audit Trails (WORM)
Implementing Write-Once-Read-Many (WORM) storage protocols to meet SEC Rule 17a-4. Every transaction, access request, and system mutation is cryptographically signed and logged, providing mathematically provable compliance guarantees during regulatory audits.
Data Segregation
Multi-tenant architectures with hard logical isolation and dedicated encryption keys per tenant, ensuring zero data bleed across Chinese walls.
High-Volume Batch Processing Modernization
Legacy mainframe batch jobs running overnight are no longer sufficient for modern banking demands. We migrate monolithic COBOL/mainframe workloads to distributed, cloud-native processing engines using Apache Spark, Flink, and cloud-native serverless orchestrators.
Our architectures reduce end-of-day (EOD) processing times from hours to minutes, utilizing massively parallel processing (MPP) capabilities while maintaining transactional consistency (ACID) across millions of ledger entries.
- Idempotent processing pipelines for guaranteed fault tolerance
- Dynamic scaling of compute nodes during peak reconciliation windows
- Real-time CDC (Change Data Capture) bridging legacy DB2 and modern datastores
Edge
Ledger
Settlement
AI-Driven Fraud Detection Algorithms
Combat sophisticated financial crimes with real-time, low-latency machine learning models. Our graph-based anomaly detection systems analyze complex transaction networks to identify money laundering (AML) rings and fraudulent authorizations in under 50 milliseconds.
Graph Neural Networks
Mapping entity relationships to detect synthetic identities and organized fraud rings through deep network analysis.
Sub-50ms Inference
Deploying highly optimized model artifacts at the edge, intercepting transactions before authorization completes.
Behavioral Biometrics
Continuously scoring user interactions and session telemetry to detect account takeover (ATO) attacks.
Protecting Trillions in Assets
Our predictive models adapt to zero-day attack vectors autonomously, dramatically reducing false positives and operational overhead.
Industry FAQs
How do you handle zero-downtime migrations for core banking systems?
We employ the Strangler Fig pattern, gradually routing traffic from the legacy monolith to microservices using a robust API gateway. Parallel run phases and real-time ledger reconciliation ensure 100% data fidelity before decommissioning legacy systems.
What is your approach to PCI-DSS compliance in cloud environments?
We utilize zero-trust network architectures, tokenization at the edge, and strictly isolated Cardholder Data Environments (CDE). All infrastructure is defined as code (IaC) with continuous compliance scanning to prevent configuration drift.
Can your fraud algorithms explain their decisions for regulatory review?
Yes. We implement Explainable AI (XAI) frameworks using SHAP values and LIME, ensuring that every declined transaction generates a human-readable rationale required by Fair Lending and Equal Credit Opportunity regulations.