AWS AI/ML Cloud Native solution for financial sector supervision
Budget: €18 – €36 EUR
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+Developers_Master_Bank_Test_Data_Sheet_PoC.docx 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+Understanding.docx 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
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+Developers_Master_Bank_Test_Data_Sheet_PoC.docx 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+Understanding.docx 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