AWS AI/ML Cloud Native solution for financial sector supervision

CLOUD-NATIVE SUPTECH PLATFORM Developer Implementation Master Specification (PoC Build Pack) 1. PURPOSE OF THIS DOCUMENT This document provides the complete technical build specification for developers implementing the Cloud-Native Regulatory System (CNRS) — a supervisory technology platform for central bank supervision, risk analytics, compliance monitoring, early-warning systems, fraud detection, and stress testing. The platform is cloud-native (AWS), modular, AI-enabled, and supports regulatory analytics aligned with Basel III, AML/CFT, and Risk-Based Supervision frameworks. 2. SYSTEM OBJECTIVES The system must: Ingest regulatory data from financial institutions securely Validate, store, and process supervisory datasets Compute prudential ratios and compliance rules Detect fraud, AML anomalies, and suspicious activity Generate early warning signals for financial instability Run deterministic and Monte Carlo stress tests Provide dashboards and alerts for supervisors Maintain full auditability, integrity, and security 3. HIGH-LEVEL CLOUD ARCHITECTURE Core components: Network Layer AWS VPC (multi-AZ) Private subnets per regulated institution Central supervisory subnet Data Layer S3 Data Lake (Raw / Processed / Curated) Redshift / Aurora (analytics storage) Object Lock for integrity Compute & Processing Lambda (validation, rules engine) EC2 (stress testing engines, Monte Carlo) Glue ETL (transform pipelines) Step Functions (workflow orchestration) Streaming & APIs API Gateway (data submissions) Kinesis (real-time data ingestion) AI / ML SageMaker (fraud detection, early warning models) Neptune (graph AML network analytics) Redshift ML (ratio prediction) Monitoring & Security IAM / RBAC KMS encryption CloudTrail / GuardDuty CloudWatch Visualization QuickSight dashboards 4. CORE DATA INGESTION PIPELINE Institution → API Gateway → S3 Raw → Lambda Validation → S3 Processed → Glue → Redshift → Dashboards Validation must check: Schema correctness Duplicate submissions Logical consistency Submission manifest integrity 5. DATASETS REQUIRED FOR POC Key regulatory datasets include: Liquidity daily datasets Capital monthly datasets Credit exposure datasets Funding maturity datasets Market FX exposure datasets Large exposure datasets Fraud alerts and transactional summaries Audit trail logs Submission manifests Prudential ratio snapshots These datasets power stress tests, early warning, compliance engines, and supervisory dashboards. For+[login to view URL] None 6. CORE ANALYTICS MODULES TO BUILD 6.1 Compliance & Prudential Rules Engine CAR, LCR, NSFR calculation Breach detection Regulatory limits monitoring Data integrity validation 6.2 Early Warning System Engine Deposit run indicators Asset quality deterioration models Profitability erosion tracking Behavioural trend monitoring 6.3 Fraud & AML Analytics Transaction anomaly detection Network graph relationship analysis Structuring detection Suspicious corridor detection 6.4 Stress Testing Engines Liquidity stress Credit stress Capital adequacy shocks Market / FX shocks Combined macro scenarios 6.5 Monte Carlo Simulation Engine Tail-risk probability estimation Correlated sector loss simulations Extreme scenario modeling 7. AI / ML MODELS REQUIRED Fraud Detection: Isolation Forest Random Forest classifiers Autoencoders Graph anomaly detection Early Warning: LSTM / ARIMA models Drift detection Cluster-based institutional risk grouping Credit & Liquidity Prediction: PD/LGD prediction models Liquidity trajectory forecasting These AI modules allow predictive supervision beyond static regulatory reporting. For+Developers+[login to view URL] None 8. SECURITY & GOVERNANCE REQUIREMENTS Full encryption at rest and transit Role-based access control per department Immutable audit logs Submission integrity hashing Zero-trust inter-institution isolation 9. DASHBOARDS REQUIRED Institution risk dashboard Sector risk heatmap Compliance breach panel Stress testing results dashboard Fraud & AML alert dashboard Early Warning trend dashboard 10. POC TESTING REQUIREMENTS Developers must support: ingestion validation tests compliance breach scenarios liquidity deterioration simulations fraud anomaly injections stress test shock scenarios performance and resilience tests 11. MINIMUM BUILD PHASE (PHASE 1) Phase 1 must deliver: Data ingestion pipeline Compliance rules engine Liquidity and capital stress tests Early Warning indicators Supervisory dashboards Basic AML anomaly detection 12. OPTIONAL PHASE 2 Monte Carlo stress testing Network AML graph analytics Profitability prediction Advanced ML forecasting Automated STR pattern detection 13. DEVELOPER DELIVERABLES The engineering team must produce: Infrastructure-as-Code deployment API submission endpoints Data validation engine Stress testing compute engines Compliance rules engine AI model pipelines Dashboards Security & audit configuration Test datasets and scripts 14. FINAL OUTCOME The completed PoC must demonstrate: Real-time supervisory analytics capability Automated regulatory compliance monitoring AI-driven early warning signals Cloud-scale stress testing capability Secure supervisory data governance This architecture enables regulators to transition from periodic reporting supervision to continuous, intelligence-driven supervision. SUPTECH PoC – DEVELOPER WORK BREAKDOWN STRUCTURE (WBS) Total Duration: ~10 Weeks 1. ENGINEERING TEAM STRUCTURE (MINIMUM) Core Team 1 Solution Architect (Cloud + SupTech) 2 Backend Engineers (APIs + Data pipelines) 1 Data Engineer (ETL + validation) 1 ML Engineer (Fraud + Early Warning) 1 DevOps Engineer (IaC + CI/CD) 1 Frontend / BI Engineer (Dashboards) Optional: 1 QA/Test Engineer 2. SPRINT PLAN (10-WEEK BUILD) Sprint 1 (Weeks 1–2) — Infrastructure Foundation Deliverables: AWS VPC setup (multi-AZ) Private subnets per institution S3 Data Lake (Raw / Processed / Curated) IAM roles and RBAC baseline KMS encryption setup Git repository initialized Terraform / CloudFormation baseline IaC Sprint 2 (Weeks 3–4) — Data Ingestion & Validation Deliverables: API Gateway ingestion endpoints Secure file submission pipeline Lambda validation engine Schema validation rules Duplicate submission detection Submission manifest checks Logging + CloudWatch integration Sprint 3 (Weeks 5–6) — Compliance & Risk Engines Deliverables: CAR / LCR / NSFR computation engine Prudential rule engine Breach detection service Regulatory ratio storage tables Supervisor alert generation service Sprint 4 (Weeks 7–8) — Stress Testing & Early Warning Deliverables: Liquidity stress test engine Credit shock engine Deposit run simulation engine Early warning risk scoring models Historical risk trend database Sprint 5 (Weeks 9–10) — AML / Fraud & Dashboards Deliverables: Fraud anomaly detection model Suspicious activity pattern detection Graph AML (optional PoC) Supervisory dashboards (QuickSight) Final system integration Security audit and penetration test PoC demonstration dataset 3. MODULE BUILD BREAKDOWN Infrastructure VPC / networking Security groups Data lake Encryption services Data Pipeline API ingestion ETL pipelines Validation rules Data transformation Analytics Engines Compliance engine Stress testing engines Early warning scoring Fraud detection models Visualization Risk dashboards Compliance dashboards Alerting panels 4. GIT REPOSITORY STRUCTURE suptech-platform/ │ ├── infrastructure/ │ ├── terraform/ │ └── cloudformation/ │ ├── ingestion/ │ ├── api_gateway/ │ └── lambda_validators/ │ ├── data_pipeline/ │ ├── etl_jobs/ │ └── schemas/ │ ├── analytics/ │ ├── compliance_engine/ │ ├── stress_testing/ │ ├── early_warning/ │ └── aml_models/ │ ├── dashboards/ │ └── quicksight_templates/ │ ├── security/ │ ├── iam_roles/ │ └── audit_logging/ │ └── docs/ ├── architecture/ ├── api_specs/ └── deployment_guide/ 5. ENVIRONMENT SETUP GUIDE AWS Accounts Dev environment Staging environment PoC production sandbox Required Services Enabled S3 Lambda API Gateway Glue Redshift / Aurora SageMaker Neptune (optional AML graph) CloudWatch GuardDuty IAM / KMS 6. CI/CD PIPELINE GitHub / GitLab repository Branch protection rules Automated Terraform deployment Lambda deployment pipelines Automated unit tests Container builds (if microservices used) 7. TESTING STRATEGY Required Tests ingestion integrity tests schema validation tests compliance rule accuracy tests stress testing scenario tests fraud detection simulation tests security penetration testing 8. DELIVERY CHECKPOINTS Week 2 — Infrastructure Ready Week 4 — Data ingestion working Week 6 — Compliance engine working Week 8 — Stress testing functional Week 10 — Full PoC demo ready 9. FINAL PoC OUTPUT REQUIRED At completion, system must demonstrate: secure supervisory data ingestion automated regulatory ratio computation early-warning institutional risk signals stress testing capability fraud anomaly detection live supervisory dashboards

Project ID: 40214861