🌟 ABC Bank Digital Lending – Enterprise Architecture Blueprint

Anand Nerurkar
1 day ago
12 min read

Updated: 19 hours ago

1. Executive Summary / Vision

Vision: Fully digital retail lending platform (personal, home, auto, education loans).
Objectives: Real-time decisioning, regulatory compliance, risk management, and scalable operations.
Strategic Goals:
1. End-to-end digital customer journey.
2. Real-time KYC, credit, fraud, AML, FinCrime checks.
3. Event-driven microservices with Data Lake analytics.
4. Full auditability and regulatory compliance.
5. Data-driven insights for portfolio and risk management.

2. Business Context

Drivers: Regulatory compliance, customer expectations, operational efficiency, real-time risk insights.
Customer Persona: Amit R — uploads PAN, Aadhaar, salary slips, bank statements, ITR; expects fast approval.
External Partners:
- Fenergo (KYC/CDD/EDD)
- CIBIL / Experian (Credit Score)
- Experian Hunter + Internal ML (Fraud)
- Actimize (AML / FinCrime)
- TCS Bancs / Finacle (CBS)

3. Scope & Objectives

Scope: Complete lending lifecycle: Application → Decision → Account → Disbursement → EMI → Repayment → Closure
Architecture: Event-driven microservices, Kafka-based topics/events, Data Lake (Raw → Curated → Analytics), security via Azure AD + SailPoint.
Objectives:
- Real-time processing with Outbox pattern
- ML-enhanced scoring (PD, LTV, EMI, Fraud, AML)
- Complete audit trails
- Scalable, resilient architecture

4. Stakeholders & Roles

Stakeholder	Role
CTO / Enterprise Architect	Platform design, tech strategy, compliance oversight
BU Heads	Define business rules, KPIs, SLAs
IT / DevOps	Implement microservices, CI/CD, monitoring
Compliance & Risk	Validate KYC, AML, Fraud workflows
Security Team	Manage IAM, encryption, and audit controls
Operations	Manual review, exception handling
External Partners	Provide KYC, Credit, AML, Fraud scoring APIs

5. Principles / Standards /Patterns

Architecture Principles:

Cloud-Native First (AKS, managed services).
- Cloud-First, API-First – all new services are cloud-native and API-enabled.
Security by Design – every microservice follows “least privilege” and is scanned in CI/CD pipelines.
- Security by Design (Zero Trust, mTLS, IAM-first).
Event-driven microservices & Write /Read Model
Trust ,But Validate every touchpoint
Compliance-Driven – regulatory obligations embedded into architecture.
- Compliance-Driven (SEBI, RBI, FATCA, AML,OFAC.,GDPR).
Reuse over Build – prefer reusing enterprise services (KYC, Credit Scoring, AML) before building anew.
Event-Driven & Real-Time – Kafka backbone for streaming data (fraud alerts, credit checks).
Data is an Asset – single source of truth (golden customer record), data lineage, audit trails.
Observability & Transparency – monitoring, logging, tracing integrated into every layer.
- Observability (logs, metrics, traces mandatory).
Resilience & High Availability (active-active, DR strategy).
Vendor-Agnostic – core services remain portable across Azure/AWS/GCP where possible.
Automation First – IaC, automated regression, auto ML retraining pipelines.
Customer-Centric – architecture optimized for faster, simpler lending journeys.
Open Standards: OAuth2.0, OIDC, TLS 1.3, ISO 27001.
Standardized Tech Stack (Spring Boot, Angular, AKS, Kafka, Redis, Cosmos DB, Postgres BDR, Fenergo, Actimize.).

Architecture Standards

Microservices Standards:
- Spring Boot, Java 17, REST/gRPC, Kafka for event streaming.
- Circuit breaker pattern (Resilience4j), API Gateway (Azure APIM).
- Idempotency for all financial transactions.
Security Standards:
- OWASP Top 10 compliance.
- Encryption (AES-256 at rest, TLS 1.3 in transit).
- Azure Key Vault for secrets.
- SailPoint-driven role lifecycle, JML (Joiner-Mover-Leaver) automation.
Data Standards:
- Master Data Management (MDM) for customer profile.
- Data quality rules defined for KYC/AML.
- GDPR-compliant PII anonymization.
DevOps Standards:
- IaC with Terraform/Bicep.
- CI/CD with gated builds, SAST/DAST, container scans.
- Blue-green & canary deployments.Security Standards:

Design & Integration Patterns

Event-Driven Pattern: Loan events → Kafka → downstream microservices (AML, Fraud).
Strangler Fig Pattern: Gradually replace legacy CBS modules with microservices.
Anti-Corruption Layer: Between new microservices and Finacle/BaNCS.
Saga Pattern: Distributed loan transaction consistency.
CQRS & Event Sourcing: For credit decisioning and fraud audit trails.
API Façade Pattern: Hide legacy CBS APIs with modern REST façade.
Batch Offload Pattern: Legacy Proc*C → Spring Batch with event triggers.

Architecture View

6. Current State (As-Is)

Monolithic loan application system
Manual KYC, credit, fraud checks
Batch-based CBS integration
Limited analytics and reporting
Minimal IAM and governance

Pain Points:

Slow processing
Data silos
Inconsistent risk scoring
Difficult compliance reporting

7. Target Architecture (To-Be)

A. Conceptual View/Architecture:

End-to-end event-driven digital lending platform.
Customer journey: Login → Apply → Document Upload → KYC/AML → Decision → Account Creation → Disbursement → EMI → Repayment → Closure.
Real-time notifications and audit logs.

B. Application View/Architecture:

Microservices:
- loan-svc, document-svc, kyc-svc, credit-svc, fraud-svc, aml-svc, loan-orchestration-svc, account-svc, payment-svc.
Kafka Topics: Each business event is a topic for isolation and ACL-based access.
Drools-based decision engine for loan approval / manual review.
External API integrations

C. Data View/Architecture:

PostgreSQL for transactions (loan_application, loan_document, loan_status, outbox_event).
Redis / NoSQL for caching.
Data Lake for Raw (JSON) → Curated (Parquet) → Analytics.
Audit service logs all events for compliance.

D. Technology View/Architecture:

AKS Multi-AZ for microservices.
Kafka for event streaming.
Azure Blob Storage + Data Lake Gen2.
Istio service mesh.
Prometheus / Grafana for monitoring.
ELK for logs.
Azure AD + SailPoint for IAM.

E. Integration View/Architecture:

External: Fenergo, CIBIL / Experian, Actimize, Experian Hunter.
Internal: CBS (TCS Bancs / Finacle), Payment Service.
API-first, secure, event-driven, auditable.

8. Operational Architecture

1. Production Environment:

AKS Multi-AZ clusters (Active-Active)
Environment separation: Dev / QA / Staging / Prod
Microservices in Docker containers

2. Disaster Recovery (DR):

PostgreSQL geo-replication + Blob GRS
Kafka MirrorMaker replication
Traffic Manager / Front Door for automatic failover
Backups: daily full + hourly incremental

3. Monitoring & Observability:

Prometheus + Grafana dashboards
Kafka topic lag monitoring
Outbox queue monitoring
ELK centralized logging
Alerts: PagerDuty / OpsGenie

4. Incident & Recovery:

Self-healing pods, CI/CD rollback
Runbooks for service failures, Kafka, Data Lake, CBS integration

5. Capacity & Scalability:

Auto-scaling microservices
Kafka partition scaling
Data Lake ingestion parallelization

6. Operational Compliance:

Audit trails for all events
Data retention policies
Access reviews via SailPoint

9. Security Architecture

Identity & Access: Azure AD (AuthN/AuthZ), JWT tokens, SailPoint governance (request/approve/recertify access).
- UI → API: Azure AD → JWT → API Gateway → Backend (mTLS enforced).
- Service → Service: Token filter, mTLS, Zero Trust.
- Kafka: SASL/PLAIN, TLS, topic ACLs.
- DBs (Postgres, Cosmos, Redis): Access via Private Link only.
- Data Security: TDE at rest (Postgres, Cosmos DB), TLS 1.3 in transit, digital signatures for flat files, checksum validation.
- Data Protection: TDE at rest, TLS in transit, digital signature + checksum for file uploaded to SFTP.
Perimeter: Azure Traffic Manager → Front Door → WAF → App Gateway.
Network Security: Private Link for DB/Redis, WAF + DDOS on App Gateway/Front Door/Traffic Manager.
Service-to-Service Security: mTLS, token filter enforcement, auto-refresh tokens.
Zero Trust: No implicit trust, least-privilege enforced.
Governance: SailPoint for RBAC, SoD, access certification.
Compliance: Logs immutable in SIEM, RBI/FIN-INS submission audit

Security & Governance

Azure AD + SailPoint for IAM and role-based governance
Kafka ACLs enforce microservice isolation
Encryption: TLS in transit, AES-256 at rest
Audit Service logs all events for compliance
Regulatory compliance: KYC, AML, FinCrime, SEBI/BFSI

10. Technology Evaluation & Selection

Component	Technology Chosen	Rationale
Messaging / Event Streaming	Kafka	Enterprise-grade, ACLs, high throughput
Microservices Runtime	Spring Boot + AKS + Istio	Cloud-native, scalable, resilient
Data Lake / Analytics	Azure Data Lake Gen2, Parquet	Standardized storage, ML-ready
Identity & Governance	Azure AD + SailPoint	Centralized IAM, SoD enforcement
Core Banking	Finacle / TCS Bancs	Enterprise BFSI standard
Decision Engine	Drools	Flexible business rules, regulatory-friendly
ML / Risk Models	Python / Internal ML	PD, LTV, EMI affordability, fraud detection

11. End-to-End Event-Driven Lending Journey (Amit R)

11. End-to-End Step-by-Step Lending Journey (Amit R)

Step 1 – Customer Login & Application:

Amit R logs in via ABC Bank portal.
Initiates loan application → triggers loan-initiated-event in Outbox → Kafka topic loan-initiated-event.
Consumers: kyc-svc, credit-svc, fraud-svc, aml-svc.
Data Lake raw ingestion: /raw/loan/amit_r.json.

Step 2 – Document Upload & OCR:

Amit R uploads PAN, Aadhaar, salary slips, bank statements, ITR.
document-svc extracts metadata via OCR → stores PDF in Blob Storage.
Event document-uploaded-event published → consumed by Loan Orchestration, Data Lake service.

Step 3 – KYC / AML / Fraud / Credit Checks:

kyc-svc calls Fenergo API → updates status → kyc-verified-event.
credit-svc calls CIBIL/Experian → internal composite credit score → credit-score-verified-event.
fraud-svc calls Experian Hunter + internal ML → fraud-clear-event.
aml-svc calls Actimize → aml-clear-event.
All events ingested into Data Lake → Curated Parquet → used by ML, analytics, and reporting.

Step 4 – Loan Decision & Manual Review:

Loan Orchestration service evaluates Drools rules: PD, LTV, EMI affordability, income/debt ratio.
Decision outcomes: loan-approved-event, loan-rejected-event, or loan-manual-review-event.
Manual review by Operations for exceptions.
Approved loans trigger CBS account creation → loan-account-created-event.

Step 5 – Disbursement & EMI:

Payment service executes fund transfer to builder / beneficiary → loan-disbursed-event.
EMI schedule created → monthly emi-generated-event, repayments → loan-repayment-event.
At tenure end → loan-closed-event.

Step 6 – Data Lake & Analytics:

Raw JSON → Curated Parquet for every event.
Used for ML retraining, dashboards, regulatory reporting.

Step 7 – Audit & Security:

Every event logged in Audit service.
Kafka ACLs ensure microservice isolation.
Access governance via SailPoint and Azure AD.

Step-by-step Timeline:

Timestamp	Event	Producing MS	Kafka Topic	Consumers	Data Lake Path
10:00:00	Login	Web Portal	–	–	–
10:02:00	Loan Initiated	loan-svc	loan-initiated-event	kyc-svc, credit-svc, fraud-svc, aml-svc	/raw/loan/amit_r.json
10:03:00	Document Upload	document-svc	document-uploaded-event	Loan Orchestration, Data Lake	/raw/documents/amit_r.json
10:05:30	KYC Verified	kyc-svc	kyc-verified-event	Loan Orchestration, Audit, Data Lake	/curated/kyc/amit_r.parquet
10:06:30	Credit Score Verified	credit-svc	credit-score-verified-event	Loan Orchestration, Audit, Data Lake	/curated/credit/amit_r.parquet
10:07:30	Fraud Clear	fraud-svc	fraud-clear-event	Loan Orchestration, Audit, Data Lake	/curated/fraud/amit_r.parquet
10:08:30	AML Clear	aml-svc	aml-clear-event	Loan Orchestration, Audit, Data Lake	/curated/aml/amit_r.parquet
10:10:00	Loan Approved	loan-orchestration-svc	loan-approved-event	CBS, Notification, Audit	/curated/loan/amit_r.parquet
10:10:30	Loan Account Created	account-svc	loan-account-created-event	Payment Service, Audit	/curated/account/amit_r.parquet
10:11:00	Loan Disbursed	Payment Service	loan-disbursed-event	Notification, Audit	/curated/payment/amit_r.parquet
Monthly	EMI Generated / Repayment	loan-orchestration-svc / Repayment Service	emi-generated-event, loan-repayment-event	Loan Orchestration, Audit, Data Lake	/curated/emi/amit_r.parquet
End of Tenure	Loan Closed	Loan Orchestration	loan-closed-event	CBS,

================================================================================

| BUSINESS LAYER |

================================================================================

| Customer Journey (Amit R) |

| - Login & Authentication |

| - Loan Application: Personal/Home/Auto/Education |

| - Document Upload (PAN, Aadhaar, Salary slips, Bank statements, ITR) |

| - Consent for KYC/CDD/EDD, Credit, Fraud, AML/FinCrime |

| - Loan Decision: Approve / Reject / Manual Review |

| - Account Creation & Disbursement |

| - EMI Generation / Repayment / Closure |

| Business Rules: |

| - PD, LTV, EMI affordability, Income-to-Debt ratio |

| - Drools-based decision rules |

================================================================================

| APPLICATION LAYER |

================================================================================

| Microservices: |

| - loan-svc: Initiates loan, updates status, writes Outbox events |

| - document-svc: Handles document upload, OCR, metadata storage |

| - kyc-svc: KYC/CDD/EDD verification via Fenergo |

| - credit-svc: Credit score verification via CIBIL / Experian |

| - fraud-svc: Fraud detection via Experian Hunter + internal ML |

| - aml-svc: AML & Financial crime checks via Actimize |

| - loan-orchestration-svc: Orchestrates workflow, integrates decisions |

| - account-svc: Loan account creation (TCS Bancs / Finacle) |

| - payment-svc: Disbursement & repayments |

| Integration & Communication: |

| - Kafka Topics/Events: loan-initiated, loan-approved, loan-rejected, |

| kyc-verified, credit-score-verified, fraud-clear, aml-clear, etc. |

| - Outbox pattern ensures consistency |

================================================================================

| DATA LAYER |

================================================================================

| Relational DB: PostgreSQL |

| - loan_application, loan_document, loan_status, outbox_event |

| NoSQL / Cache: Redis |

| Data Lake Gen2: |

| - Raw Layer: JSON (/raw/loan/amit_r.json, /raw/documents/amit_r.json) |

| - Curated Layer: Parquet (/curated/kyc/amit_r.parquet, /curated/credit/amit_r.parquet, ...) |

| - Analytics Layer: ML scoring, composite score, PD/LTV/EMI calculations |

| Audit Service: Logs all events for compliance |

================================================================================

| TECHNOLOGY LAYER |

================================================================================

| Cloud: Azure |

| Containerization: Docker |

| Orchestration: AKS + Istio Service Mesh |

| Messaging: Kafka (topic-level ACLs) |

| Storage: Azure Blob Storage + Data Lake Gen2 |

| Monitoring: Prometheus + Grafana |

| Logging: ELK stack |

| CI/CD: Azure DevOps |

================================================================================

| SECURITY LAYER |

================================================================================

| Authentication: Azure AD |

| Authorization: Role-based access via Azure AD |

| Identity Governance: SailPoint for SoD, access reviews, lifecycle management |

| Kafka ACLs: Microservice/topic/event isolation |

| Encryption: TLS in transit, AES-256 at rest |

| Audit Trail: All events logged in Audit Service + Data Lake |

================================================================================

| OPERATIONAL LAYER |

================================================================================

| DR Strategy: PostgreSQL geo-replication, Azure Blob GRS, Kafka MirrorMaker, |

| Traffic Manager failover |

| Monitoring: Kafka lag, Outbox queue, Prometheus metrics, Grafana dashboards |

| Incident Management: Self-healing pods, CI/CD rollback, runbooks |

| Capacity & Scalability: Auto-scaling microservices, Kafka partitions |

| Compliance: Audit trails, data retention, SailPoint access reviews |

================================================================================

| ANALYTICS / ML LAYER |

================================================================================

| Composite Scoring: Credit, Fraud, AML, Internal ML models |

| Risk Calculations: PD, LTV, EMI affordability, Income-to-Debt ratio |

| Predictive Insights: Portfolio risk, early warning, fraud trends |

| Data Lake Analytics: Curated Parquet files for ML training, regulatory reporting|

| Real-time Dashboards: Operational metrics, event streams |

================================================================================

[10:00] Customer Login / Authentication

└─ Web Portal / Auth Service

└─ Authenticated via Azure AD

└─ SailPoint enforces role & access governance

[10:02] Loan Application Initiated

└─ loan-svc

├─ Writes Outbox Event: loan-initiated-event

├─ Kafka Topic: loan-initiated-event

│ ├─ Consumed by:

│ │ ├─ kyc-svc

│ │ ├─ credit-svc

│ │ ├─ fraud-svc

│ │ └─ aml-svc

└─ Data Lake Raw: /raw/loan/amit_r.json

└─ Curated Layer: /curated/loan/amit_r.parquet

[10:03] Document Upload

└─ document-svc

├─ OCR & metadata extraction

├─ Writes Outbox Event: document-uploaded-event

├─ Kafka Topic: document-uploaded-event

│ ├─ Consumed by:

│ │ ├─ loan-orchestration-svc

│ │ └─ audit-svc

└─ Data Lake Raw: /raw/documents/amit_r.json

└─ Curated Layer: /curated/documents/amit_r.parquet

[10:05] KYC Verification

└─ kyc-svc (Fenergo)

├─ Writes Outbox Event: kyc-verified-event

├─ Kafka Topic: kyc-verified-event

│ ├─ Consumed by:

│ │ ├─ loan-orchestration-svc

│ │ ├─ audit-svc

│ │ └─ data lake service

└─ Data Lake Curated: /curated/kyc/amit_r.parquet

[10:06] Credit Score Verification

└─ credit-svc (CIBIL / Experian)

├─ Writes Outbox Event: credit-score-verified-event

├─ Kafka Topic: credit-score-verified-event

│ ├─ Consumed by:

│ │ ├─ loan-orchestration-svc

│ │ ├─ audit-svc

│ │ └─ data lake service

└─ Data Lake Curated: /curated/credit/amit_r.parquet

[10:07] Fraud Check

└─ fraud-svc (Experian Hunter + internal ML)

├─ Writes Outbox Event: fraud-clear-event

├─ Kafka Topic: fraud-clear-event

│ ├─ Consumed by:

│ │ ├─ loan-orchestration-svc

│ │ ├─ audit-svc

│ │ └─ data lake service

└─ Data Lake Curated: /curated/fraud/amit_r.parquet

[10:08] AML / Financial Crime Check

└─ aml-svc (Actimize)

├─ Writes Outbox Event: aml-clear-event

├─ Kafka Topic: aml-clear-event

│ ├─ Consumed by:

│ │ ├─ loan-orchestration-svc

│ │ ├─ audit-svc

│ │ └─ data lake service

└─ Data Lake Curated: /curated/aml/amit_r.parquet

[10:10] Loan Decision (Approve / Reject)

└─ loan-orchestration-svc

├─ Applies Drools Rules + Composite Score (PD, LTV, EMI, Income-to-Debt)

├─ Writes Outbox Event: loan-approved-event / loan-rejected-event

├─ Kafka Topic: loan-approved-event / loan-rejected-event

│ ├─ Consumed by:

│ │ ├─ account-svc

│ │ ├─ payment-svc

│ │ ├─ notification-svc

│ │ └─ audit-svc

└─ Data Lake Curated: /curated/loan/amit_r.parquet

[10:10] Loan Account Creation

└─ account-svc (TCS Bancs / Finacle)

├─ Writes Outbox Event: loan-account-created-event

├─ Kafka Topic: loan-account-created-event

│ ├─ Consumed by:

│ │ ├─ payment-svc

│ │ └─ audit-svc

└─ Data Lake Curated: /curated/account/amit_r.parquet

[10:11] Loan Disbursement

└─ payment-svc

├─ Writes Outbox Event: loan-disbursed-event

├─ Kafka Topic: loan-disbursed-event

│ ├─ Consumed by:

│ │ ├─ notification-svc

│ │ └─ audit-svc

└─ Data Lake Curated: /curated/payment/amit_r.parquet

[Monthly] EMI Generation / Repayment

└─ loan-orchestration-svc / repayment-svc

├─ Writes Events: emi-generated-event / loan-repayment-event

├─ Kafka Topics: emi-generated-event / loan-repayment-event

│ ├─ Consumed by: loan-orchestration-svc, audit-svc, data lake service

└─ Data Lake Curated: /curated/emi/amit_r.parquet

[End of Tenure] Loan Closure

└─ loan-orchestration-svc

├─ Writes Outbox Event: loan-closed-event

├─ Kafka Topic: loan-closed-event

│ ├─ Consumed by: CBS, audit-svc, data lake service

└─ Data Lake Curated: /curated/loan/amit_r.parquet

----------------------------------------

| Analytics / ML

----------------------------------------

- ML scoring layer consumes curated data: Credit, Fraud, AML

- Produces composite score (PD, LTV, EMI, Income/Debt)

- Feeds Drools rules for automated loan decision

- Predictive insights: portfolio risk, early warning, fraud trends

- Retraining scheduled via data lake pipelines

----------------------------------------

| Audit & Security

----------------------------------------

- All events logged in Audit Service

- Kafka ACLs ensure topic/event isolation per microservice

- Azure AD + SailPoint enforces authentication, authorization, SoD

- TLS encryption + AES-256 at rest

----------------------------------------

| Operational / DR

----------------------------------------

- AKS Multi-AZ deployment

- PostgreSQL geo-replication

- Azure Blob GRS for storage

- Kafka MirrorMaker for replication

- Prometheus / Grafana monitoring

- ELK stack logging

- CI/CD rollback & runbooks

- Auto-scaling microservices & Kafka partitions+

Notes on Event-Driven Architecture:

Outbox Pattern: Every microservice writes to its own Outbox table first → Kafka poller reads pending events → publishes to respective topic.
- Outbox → Kafka → Microservice → Data Lake → ML / Analytics → Auditis the core pattern for all events.
Topic ACLs: Each microservice can only consume events it is authorized for. E.g., loan-initiated-event consumed only by kyc-svc, credit-svc, fraud-svc, aml-svc.
- Kafka ACLs ensure that only authorized microservices consume each event/topic.
Data Lake Flow:
- Raw Layer: Stores JSON as-is from event → /raw/...
- Curated Layer: Transformed / enriched → /curated/... (Parquet format)
ML Scoring / Analytics:
- Internal ML + external credit/fraud/AML APIs
- Produces composite score (PD, LTV, EMI, income-to-debt ratio)
- Feeds Drools rules for automated loan decision
Audit / Security / Governance:
- All events logged in Audit Service
- IAM via Azure AD + SailPoint ensures SoD and access reviews
- Data encrypted in transit and at rest
Drools engine applies business rules for approval, rejection, or manual review.
Operational layer ensures DR, monitoring, auto-scaling, compliance.
Security & governance enforced end-to-end (Azure AD + SailPoint + encryption + audit)

12. Security & Identity Governance

Azure AD: Authentication, role-based authorization.
SailPoint: Identity governance, role lifecycle, SoD, access reviews.
Kafka ACLs: Microservices restricted to only their relevant topics/events.
Encryption: TLS in transit, AES-256 at rest.
Audit Trail: Every event logged in Audit Service + Data Lake.
Regulatory Compliance: KYC, AML, FinCrime, SEBI, GDPR.

15. Conclusion

The ABC Bank Digital Lending EA Blueprint now captures:

Executive vision and business context – aligned to BFSI standards and regulatory requirements.
Complete end-to-end event-driven lending journey for Amit R, including external integrations, internal ML scoring, and microservices orchestration.
Architecture Views: Conceptual, Application, Data, Technology, Integration, Security, Operational.
Security & Governance: Azure AD + SailPoint, Kafka ACLs, encryption, audit trails.
Operational Excellence: DR, monitoring, auto-scaling, SLA management, incident response.
Technology Rationale: Stack selection justified for scalability, security, and compliance.

Outcome:

Fully realistic, enterprise-grade digital lending architecture, suitable for BFSI standards
Supports multiple loan types, drools-based decision rules, ML scoring, event-driven orchestration, and compliance-ready audit trails.
Provides a data foundation for analytics, reporting, and future AI/ML enhancements.

🌟 ABC Bank Digital Lending – Enterprise Architecture Blueprint

1. Executive Summary / Vision

2. Business Context

3. Scope & Objectives

4. Stakeholders & Roles

5. Principles / Standards /Patterns

Design & Integration Patterns

6. Current State (As-Is)

7. Target Architecture (To-Be)

8. Operational Architecture

9. Security Architecture

Security & Governance

10. Technology Evaluation & Selection

11. End-to-End Event-Driven Lending Journey (Amit R)

11. End-to-End Step-by-Step Lending Journey (Amit R)

12. Security & Identity Governance

15. Conclusion

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