Personal Banking Modernization

Personal Banking Modernization Architecture Document

1. Vision and Strategy

Modernize legacy personal banking systems into a microservices-based architecture hosted on Azure Cloud, ensuring scalability, high availability, regulatory compliance, enhanced customer experience, and future-readiness using AI/ML-driven intelligence and observability tools.

Business Outcomes & KPIs

• Improve system uptime (KPI: 99.99% availability)

• Accelerate digital onboarding (KPI: Onboarding time < 5 minutes)

• Enhance loan approval efficiency (KPI: 80% automation rate)

• Reduce fraud (KPI: 90%+ fraud detection accuracy)

• Boost customer satisfaction (KPI: CSAT > 4.5)

2. Capability Map with KPI and Service Mapping

Capability	Services	Azure Services	KPIs
Account Management	Account, Profile, Notification	AKS, Azure SQL, Key Vault, Azure Monitor	SLA, Avg response time, Account success
Loan Management	Origination, Credit Score, Docs	AKS, Azure ML, Cosmos DB, Blob Storage	Loan approval rate, ML accuracy
Transactions	Txn, Ledger, Audit	AKS, Azure SQL, Service Bus, Azure Monitor	Latency, success rate, errors
Customer Support	Chatbot, Ticket, Feedback	AKS, Azure Bot, Cosmos DB, App Insights	Resolution time, CSAT, backlog
Fraud Detection	Analysis, Alerts, Scoring	AKS, Azure ML, Synapse, Event Hub	Detection rate, FPR, detection time

3. High-Level Architecture Flow (Azure Cloud)

External Request Path:

1. Azure CDN: Cache static content.

2. Azure Traffic Manager: Global DNS-based load balancing.

3. Azure Front Door: HTTP routing and WAF.

4. Azure Application Gateway: Path-based routing, SSL termination.

5. Azure Load Balancer: Directs to AKS nodes.

6. Azure AKS (Istio): Hosts microservices; Istio for mesh.

Internal & Data Layer:

• Kafka: Event-driven communication.

• Azure SQL, Cosmos DB, Blob: Persistent storage.

• Azure ML/Synapse: AI/ML analytics.

Monitoring & Observability:

• Azure Monitor, Grafana, Prometheus: Metrics.

• ELK Stack: Log aggregation.

Networking & Security:

• Azure VNet: Divided into subnets:

  • Public: CDN, Front Door, Gateway

  • Private: AKS, DBs, Kafka

• Firewall, NSG: Inbound/outbound control

DevOps & CI/CD:

• Azure DevOps Pipelines: Code build, test, deploy.

Availability:

• Active-Active Multi-Region: HA, DR, geo-failover

4. Enterprise Risk Register (Top 10 of 50)

Risk Category	Risk Description	Priority	Mitigation Plan
Security	Data breaches via misconfigured services	High	Apply WAF, NSG, Firewall, Role-based access
Compliance	Non-adherence to RBI/SEBI guidelines	High	Regular audits, compliance monitoring tool
Tech Debt	Legacy systems integration failure	Medium	Refactor into adapters, use strangler pattern
Availability	AKS cluster crash	High	Active-active deployment, auto-scaling, HA config
Performance	High latency during traffic spikes	High	Use Azure Autoscale, CDN, cache optimization
Fraud	Sophisticated fraud attacks	High	AI/ML for fraud detection, behavior analytics
Vendor Lock	Overdependence on Azure-specific tools	Medium	Abstracted service layer, cloud-agnostic design
Integration	API failures between services	High	Retry logic, circuit breakers, monitoring
Monitoring	Lack of real-time insights	Medium	Implement ELK, Azure Monitor, Grafana, alerts
Data Quality	Inconsistent account or loan data	High	Central MDM, validation rules, ETL checks

(Full 50 risks in Excel sheet separately provided)

5. Use Case Architecture Breakdown

5.1 Account Management

• Microservices: Profile, Account, Notification

• Data Stores: Azure SQL, Azure Key Vault

• User Flow:

  1. User logs in via secure token (Azure AD)

  2. Account Service fetches user details from Azure SQL

  3. Notification Service confirms via email/SMS

5.2 Loan Management

• Microservices: Origination, Credit Scoring, Verification

• AI/ML Integration: Azure ML for scoring

• User Flow:

  1. User submits loan request via frontend

  2. Origination Service validates, invokes Credit Score API

  3. ML model evaluates and returns score

  4. Decision sent, document verification triggered

5.3 Fraud Detection

• Microservices: Risk Analyzer, Alerts

• AI/ML: Detect anomalies in transaction behavior

• Flow:

  1. Kafka streams transactions

  2. AI model analyzes in Azure Synapse

  3. High-risk transactions flagged, alerts sent