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Enterprise Blueprint-Digital Lending @ABC Bank
- Anand Nerurkar
- Sep 22
- 17 min read
1. Business Context
Customer: Amit R applies for a home loan.
Business Goals: Seamless digital lending, faster approval, low fraud, compliance by design, operational efficiency, and customer trust.
Strategic Drivers:
Reduce loan approval TAT (Turnaround Time) from 7 days → 1 day.
Improve fraud detection accuracy by 25%.
Ensure SEBI/RBI/FIN-IND compliance reporting is automated.
Strengthen security posture under zero-trust.
2 Enterprise Strategy
Transform ABC Bank into a cloud-native, digital-first lender.
Enable regulatory compliance by design (RBI, FIU-IND, FATCA, OFAC).
Build open, API-driven integrations with ecosystem partners (Fenergo, Actimize, CIBIL, Experian, Hunter).
Legacy modernization:
EJB → Spring Boot Microservices
PL/SQL Stored Procs → REST APIs / Spring Data JPA
Proc*C Batch → Spring Batch
Oracle Forms → Angular Frontend
Automated conversion accelerators where possible.
Implement DevOps + DevSecOps pipelines for CI/CD, IaC, policy-as-code.
Deploy AI/ML & GenAI for fraud detection, credit risk,customer servicing,ABC Bank Advisor(Loan FAQ,Mutual Fund FAQ,Document Summerization) .
3 Business–IT Alignment with measurable KPI
Business Objective: Faster loan approvals, better compliance, reduced fraud.
IT Enablement: API-first microservices, scalable AKS/Kubernetes deployment, automated compliance reporting, customer self-service via GenAI.
Translate business goals into capability KPIs — e.g., reduce onboarding drop-offs → measure % complete onboarding vs started; reduce fraud → fraud loss % per million disbursed.
Business Capabilities
High-Level Capabilities
Customer Onboarding & KYC
Loan Origination & Assessment
Risk & Fraud Management
Loan Decisioning
Disbursement & Agreement Management
Compliance & Reporting
Identity Governance & Access Control
Monitoring & Operations
Business Capability → Service Mapping
Capability | Services (Microservices) |
Customer Onboarding & KYC | Customer Profile Svc, KYC Svc, Document Upload Svc |
Loan Origination | Loan Application Svc, Credit Bureau Svc, Income Verification Svc |
Risk & Fraud Mgmt | Fraud Risk Model Svc, Credit Risk Model Svc, Feature Store |
Loan Decisioning | Loan Decision Engine, Business Rule Engine |
Agreement & Disbursement | Loan Agreement Svc, Disbursement Svc |
Compliance | Fenergo Compliance Svc, Actimize AML Svc |
Identity Governance | Azure AD, SailPoint Governance Svc |
Ops & Monitoring | Logging Svc, Audit Svc, Kafka Messaging, ELK, Grafana |
Capability → Application Mapping
Capability | Applications |
Onboarding | Angular UI, Spring Boot Microservices, Fenergo Portal |
Risk & Fraud Mgmt | Azure ML, Fraud Analytics App |
Loan Decisioning | Drools Rules Engine, Decision API |
Compliance | Actimize AML, Fenergo Compliance |
Identity Governance | SailPoint IdentityIQ, Azure AD Portal |
Ops | Azure Monitor, Grafana, ELK |
Establish OKRs / KPIs & owners — each capability has a business owner (e.g., Lending Head) and a technical owner (e.g., Service Lead).
Core KPIs (tracked per capability)
Business: Onboarding TAT, Conversion rate, NPS, Fraud loss %.
Delivery: Release frequency, Lead time, Defect escape rate.
Security: % services with SAST pass, average time to fix critical vuln, IAM recertification %.
Compliance: % reports accepted by FIU-IND/RBI, audit SLA fulfillment.
Operational: Uptime, MTTR, latency percentiles.
Design KPI dashboard — single pane for leadership showing Business / Delivery / Security / Compliance / Operational metrics.
PI planning & KPI reviews — align development increments to KPI improvement targets and review outcomes at program increments.
Value realization tracking — after a capability release, measure actual KPI delta vs baseline and feed into roadmap reprioritization.
Reporting to C-suite & regulators — perform monthly scorecard updates and provide audit packets on demand.
Artifacts produced
KPI mapping table (capability → KPI → target → owner)
Dashboards (Power BI / Grafana)
Quarterly value realization reports
Stakeholders
Business Heads, PMO, EA, CIO/CTO.
KPI / Acceptance
Demonstrable movement on business KPIs tied directly to delivered capabilities; acceptance by business sponsors.
4. Architecture Views
1. Conceptual Architecture
High-level business-driven view showing domains, actors, flows.
Channels: Web (Angular), Mobile App
Access Mgmt: Azure AD, SailPoint (governance)
Loan Origination System (LOS): Loan Orchestration, Decision Engine
Risk & Compliance: Credit Bureau, Fraud Vendors, AML (Actimize), Internal ML Service
Core Banking System (CBS): Loan servicing, disbursement, repayment
Compliance Platforms: Fenergo, Actimize, RBI/FIN-INS
Data Layer: Feature Store, Data Lake, Redis, Cosmos DB, PostgreSQL
Integration & Messaging: Kafka (secured), Outbox pattern
Security & Governance: WAF, mTLS, TLS, Private Links, IAM
2. Current Architecture (As-Is)
Monolithic LOS tightly coupled with CBS.
Manual loan processing, high dependency on loan officers.
Limited Credot/Fraud/API integration with bureaus/vendors.
Batch AML checks (delayed).
No centralized Feature Store (data silos).
Governance manual, compliance reporting semi-automated.
3. Target Architecture (To-Be)
Objective: design an Azure-native, secure, resilient, multi-region architecture that supports the lending journey and integrations (Finacle/BaNCS, Fenergo, Actimize).
Architecture decisions & steps
Platform baseline — AKS (Kubernetes) for microservices, Azure API Management for external/internal API gateway, Azure Service Bus or Kafka (Event Hubs) for async/event streaming, Azure postgress bdr / CosmosDB for data persistence as appropriate, Azure Key Vault for secrets, Azure Front Door for global ingress.
Networking & connectivity — Hub-spoke VNets, ExpressRoute / VPNs to connect to on-prem Finacle/BaNCS and partner networks, private endpoints for PaaS.
Resilience & DR — active-active design across regions, geo-replication for databases and blob storage, automated failover playbooks.
Security zones — micro-segmentation via Istio/Service Mesh, WAF in front of APIs, dedicated jump hosts, conditional access via Azure AD.
Observability — Application Insights + Prometheus + Grafana + ELK stack for logs/traces/metrics, centralized dashboards for KPIs.
Cost & governance — Azure Policy + Blueprints + tagging standards to enforce cost controls and compliance.
Artifacts produced
High-level Azure architecture diagram (ingress → API MGMT → AKS → databases → partner connectors)
Landing zone templates (Terraform / ARM / Bicep)
Networking & security topology
DR & backup strategy doc
Stakeholders
Cloud Architect, Security, Network, Cost Governance, Ops.
KPI / Acceptance
Achieve required uptime SLA; all critical services deployable via IaC into landing zone with policy compliance.
Cloud-Native Microservices on AKS (Spring Boot).
API-first strategy (REST/GraphQL secured with JWT + mTLS).
Real-time Events on Kafka (secured with ACLs).
Feature Store for ML models (credit risk, fraud).
Zero Trust Security (token validation, private links, TLS everywhere).
Automated Compliance (Actimize + Fenergo integration).
Actimize integrated for AML & suspicious activity detection.
Fenego Integration for KYC/CDD/EDD
Digital Disbursement & Servicing with TCS BANCS via adapters.
End-to-End Governance with SailPoint for roles, access, SoD.
Observability: ELK, Prometheus, Grafana,SIEM
4. Integration Architecture
Core Banking → Finacle / TCS BaNCS Loan Module.
AML/KYC → Fenergo.
Fraud Detection → Actimize.
eSign & Aadhaar Vault → NSDL/eMudhra API.
Credit Bureau → CIBIL/Experian APIs.
Payment Gateway → NPCI/UPI integration.
5. Operational Architecture
CI/CD: Azure DevOps/DevSecOps pipelines, infra as code (Terraform/Bicep).
Monitoring: Azure Monitor + Grafana dashboards.
Resilience: Active-Active AKS clusters, BDR PostgreSQL, Redis clustering.
24x7 Availability with AKS Active-Active across regions.
Kafka HA clusters with topic-level ACLs.
Redis Enterprise for caching.
Disaster Recovery: RPO < 15 mins, RTO < 30 mins, geo-redundant DBs.
Incident Mgmt: Central SIEM, SOC escalation flows.
Runbooks: Auto-healing pods, scaling rules, operational dashboards.
6. Security Architecture & Threat Modelling
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 trail.
Threat Modeling (Security by Design)
Framework: STRIDE + OWASP Top 10 integrated in design reviews.
Examples:
Spoofing: Fake loan applications → Mitigation: Aadhaar OTP, PAN API validation, Fenergo KYC.
Tampering: Loan data manipulation → Mitigation: Hashing, immutability with blockchain ledger (future roadmap).
Repudiation: User denies transaction → Mitigation: Non-repudiation via digital signature (eSign, Aadhaar).
Information Disclosure: PII leaks → Mitigation: Data masking, tokenization, field-level encryption.
Denial of Service: Loan portal downtime → Mitigation: Azure Front Door + CDN + DDoS Protection.
Elevation of Privilege: Unauthorized access → Mitigation: RBAC + PAM (Privileged Access Management)
🔹 Enterprise Architecture – Lending Journey for Amit R
1. Business Layer (Capabilities & Journey)
Customer Journey:
Amit R logs into Digital Lending Portal (Angular UI).
Submits KYC & Loan Application.
Credit Risk & Fraud Risk evaluation.
Loan Decision Engine evaluates PD, LTV, Income-to-Debt Ratio, EMIAffordability.
Decision → Low Risk = Auto-Approve, High Risk = Reject, Medium = Manual Review.
Loan Agreement signed → Disbursement triggered.
Compliance reports generated (Fenergo, Actimize) → submitted to regulators.
Business Capabilities:
Customer Onboarding & KYC.
Loan Origination.
Risk & Fraud Management.
Loan Decisioning.
Agreement & Disbursement.
Compliance & Regulatory Reporting.
Identity Governance & Audit.
Monitoring & Operations.
2. Application Layer (Microservices & Systems)
UI & API Gateway: Angular UI → Azure AD login → JWT token → API Gateway (Spring Cloud Gateway).
Microservices (Spring Boot on AKS):
Customer Profile Svc.
KYC Svc (integrated with Fenergo).
Loan Application Svc.
Credit Bureau Integration Svc.
Fraud Risk Model Svc.
Credit Risk Model Svc.
Loan Decision Engine (Drools).
Loan Agreement Svc.
Disbursement Svc.
Compliance Services (Actimize, Fenergo).
Identity Governance: SailPoint → Access request/approval/review portal.
Compliance & Reporting:
Fenergo (daily compliance reports).
Actimize AML/CTR/NTR/STR/CBWR processing.
3. Data Layer (Data Stores & Flows)
Operational Databases:
Postgres BDR → Loan/Customer/Transaction data (active-active).
Redis Enterprise → Session caching, decision engine performance.
Cosmos DB → Fraud/behavioral patterns, JSON documents.
Feature Store:
Central repository for credit history, fraud patterns, bureau score, customer profile.
ML models read/write updated features.
Streaming Data:
Kafka → Loan application events, fraud alerts, compliance triggers.
Compliance Data:
Flat files generated from CBS with timestamp+checksum+digital signature.
SFTP (SSH key secured) → picked by Actimize ingestion layer → ETL → Actimize UDM.
4. Technology Layer (Infra & Platforms)
Cloud & Infra:
Azure AKS → Deploy Spring Boot services.
Azure Front Door + Traffic Manager → Global routing + failover.
Azure App Gateway (WAF enabled) → API traffic protection.
Messaging & Integration:
Kafka (with TLS, topic ACLs, RBAC).
Databases & Storage:
Postgres BDR (geo-replication).
Redis Enterprise.
Cosmos DB.
DevOps & Automation:
Azure DevOps Pipelines (CI/CD, IaC).
Azure Blueprints & Policies → compliance guardrails.
Monitoring:
ELK Stack for logs.
Prometheus + Grafana for metrics.
Azure Monitor & Sentinel for security analytics.
5. Security & Governance Layer
Identity & Access:
Login via Azure AD (MFA + Conditional Access).
JWT tokens for service-to-service calls.
SailPoint → Governance (access request, approval workflow, SoD enforcement, periodic recertification).
Data Security:
TLS 1.3 in transit.
TDE at rest (Postgres, Redis, Cosmos DB).
Digital signatures + checksum for CBS flat files.
Network Security:
All DBs via Private Link.
Kafka via TLS + RBAC.
WAF + DDOS protection on Front Door, App Gateway, Traffic Manager.
Service-to-Service Security:
mTLS between microservices.
Token filter at each hop (check token validity).
Refresh token if expired.
Zero Trust:
Least privilege enforced.
No implicit trust between services.
Governance:
Azure Policy + Blueprint for compliance.
SailPoint portal for user access governance.
Audit trails via ELK + immutable logs.
Lending Journey for Amit R +------------------+
| Amit R (UI) |
| Angular Frontend |
+------------------+
|
| Login via Azure AD (JWT Token + MFA)
v
+------------------+
| Loan Application |
| Enter Loan Amt & |
| Tenure, Consent |
+------------------+
|
| Upload Documents (PAN, Aadhaar, Income)
| Metadata stored in Postgres/Blob (TDE + Private Link)
v
+------------------+
| LoanService MS |
| Spring Boot AKS |
+------------------+
|
| Emit Outbox Event: loan-initiated
| Retry/DLQ Handling
v
+------------------+
| Kafka Event Bus |
| TLS + ACL + RBAC |
+------------------+
|
+-----------+-----------+
| | |
v v v
Redis Cache Cosmos DB Audit Table
(Private Link) (TDE) (Logs + Trace)
|
+-----------------------------------+
| Event Consumers: KYC, Credit, |
| Fraud, AML, FinCrime MS |
+-----------------------------------+
| Parallel Execution: |
| - KYC: Fenergo Adapter -> done |
| - CreditScore: CIBIL API -> done |
| - FraudScore: Experian -> done |
| - AML: Actimize -> done |
| - FinCrime: Actimize -> done |
v
+------------------------+
| LoanEvaluation MS |
| Listens on *.done events|
| Emit internal-ml-event |
+------------------------+
|
| Internal ML Service
| - Inputs: CustomerID + Credit/Fraud Score
| - Feature Store Lookup (Tx, Obligations, Pattern)
| - Outputs: PD, Internal Fraud, External Fraud
v
+------------------------+
| Loan Decision Engine |
| Apply Business Rules: |
| LTV, EMI, Collateral, |
| Income/Debt, Regulatory|
| Decision: Approve / |
| Reject / Manual Review|
+------------------------+
|
+----------+------------+
| |
v v
Loan Agreement MS Manual Review
- eSign & Emit loan-signed
|
v
+---------------------+
| Loan Account MS |
| Call TCS BANCS |
| Emit loan-acct-created
+---------------------+
|
v
+---------------------+
| Disbursement MS |
| Builder request |
| Loan Officer Approve|
| Saga: Fund Transfer |
| EMI Scheduled |
+---------------------+
|
v
Notifications (Customer + Builder)
Internam ML Flow
[Amit R submits loan application]
|
v
[LoanEvaluation MS listens for *.done events]
|
v
Emit: internal-ml-score-requested
|
v
[Internal ML Service]
- Input: CustomerID, CreditScore, FraudScore
- Feature Store Lookup: Tx history, Obligations, Patterns
- Models:
* Logistic Regression → Probability of Default
* Random Forest → Internal Credit/Fraud Risk
* Gradient Boosting → External Fraud / Anomaly Score
- Output: PD, Internal Fraud, External Fraud
v
Emit: ml-score-done -> consumed by Loan Decision Engine
5. 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).
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.
Standards & checklists — coding standards, naming conventions, API contract standards (OpenAPI), protobuf or Avro for events, logging/trace format (W3C TraceContext).
Publish reference patterns — API pattern (Gateway + Contract + Versioning), Event pattern (idempotent consumers, outbox), Data pattern (master record, change data capture), Integration pattern (sync/async fallback).
Policy and enforcement — integrate standards into CI/CD via automated checks (linting, OpenAPI conformance, contract tests, SAST, IaC linting).
KPI dashboard — define and implement dashboards for Business, Delivery, Security, Compliance, Operational KPIs (see list below).
Periodic reviews — architecture compliance reviews via EAB; exceptions recorded and time-boxed.
🔹 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.
6. Technology/Framework/Tool Evaluation & Selection
Frontend: Angular (enterprise-grade, modular).
UI Modernization: Oracle Forms/ExtJs → Angular
Backend: Spring Boot microservices (Java 17).
Batch Modernization: Proc*C → Spring Batch
Legacy Code Conversion: Automated tools (EJB/PL/SQL → Java Microservices, Trigger → Event Driven Flow)
Integration: Kafka (enterprise messaging),Batch Job,ETL Job,API,Adapter,Connector
Data:
Postgres BDR over Oracle RAC → Lower TCO, active-active replication.
Cosmos DB (NoSQL)- Multi region Read/write
Enterprise Redis - Active Active Replication across region.
Azure Blob - Document Store
Compliance: Fenergo (KYC-RBI), Actimize (AML/FinCrime -FIU-IND- Finance Ministry- Goverment of India).
IAM: Azure AD + SailPoint.
Security: TLS/mTLS, WAF, Private Links, SIEM.
Deployment: AKS, Azure DevOps, Terraform.
Fenergo → Strong KYC/CDD/EDD automation.
Actimize → Enterprise AML and suspicious activity monitoring.
SailPoint → Governance (access request, certification, SoD enforcement).
AI/GenAI Framework →
ML: Internal ML service on AKS, Feature Store for inputs.
Loan FAQ + advisory chatbot.
TOGAF ADM → Enterprise Architecture development (vision → implementation).
SABSA → Security Architecture.
COBIT 2019 → Governance & Risk Control.
ITIL v4 → Service Management.
NIST CSF → Cybersecurity posture improvement.
7. Skills Assessment
Cloud & DevOps: Kubernetes, AKS, Terraform, Azure DevOps /DevSecOPs→ Strong gap in current IT.
Data & ML: Feature Store, ML Ops → Medium gap.
Microservices & APIs: Java 17, Spring Boot → Adequate.
Compliance Tools: Actimize, Fenergo → Low maturity (training needed).
Security & IAM: Azure AD, SailPoint, Zero Trust → Skill gap exists.
8. Guided DevOps, DevSecOps & AI/ML adoption
Objective: make releases fast, safe, repeatable; operationalize ML models with governance and explainability.
DevOps / DevSecOps steps
Toolchain selection & baseline — GitHub/Azure Repos, Azure DevOps pipelines, Terraform, Helm, Argo CD for GitOps, SonarQube, Trivy/Aqua, OWASP ZAP.
Pipeline design — build → unit tests → SCA → SAST → container build → IaC scan → DAST (staging) → approval → deploy to AKS. Implement security gates as failing conditions.
Secrets & keys — keys in Azure Key Vault; no plaintext secrets in pipelines.
Shift-left testing — contract tests, component tests, and performance tests in CI.
Production safety — blue/green deployments, canaries, automated rollback on health checks.
SRE practices — SLIs/SLOs, error budgets, runbooks, incident management (PagerDuty), automated remediation playbooks.
Pipeline as code & policy — policy-as-code (OPA) to enforce security/compliance in CI.
AI/ML steps
Data pipeline — defined sources (KYC, bureau, transaction), governance approvals, data cataloging, and anonymization for training.
Model development — experiments in controlled environments, reproducible pipelines (MLflow / Kubeflow).
Explainability & fairness — integrate SHAP/LIME to provide model explanations for underwriting decisions (critical for regulator & audit).
MLOps — model CI/CD, automated validation, drift detection, production monitoring of model metrics, versioning, and rollback.
Human-in-the-loop — manual review queues for edge cases and continuous annotation for retraining.
GenAI for Advisor — RAG (Retrieval-Augmented Generation) for factual QA; guardrails to prevent hallucinations; log prompts and responses for audit.
Artifacts produced
CI/CD pipeline templates & security gate definitions
SRE dashboards and runbooks
ML model registry, validation reports, XAI reports
GenAI design doc (RAG, guardrails, logging)
Stakeholders
DevOps Lead, Security, Data Science, Product Owners, Compliance.
KPI / Acceptance
Deployment frequency, lead time for changes, % builds passing security gates, model AUC/precision/recall with XAI coverage, reduced manual reviews.
9.Governance & Compliance
Architecture Governance Board: Reviews designs against standards.
Azure Policy + Blueprint: Enforce compliance (RBAC, encryption, geo-restriction).
Audit Trail: Centralized logging of all KYC, AML, disbursement, and advisory transactions.
Azure Policy + OPA: Continuous compliance enforcement.
DevSecOps Gating: Security checks as part of CI/CD.
Audit & Reporting Layer: End-to-end logging with immutability
10 Partner / vendor orchestration (Finacle, BaNCS, Fenergo, Actimize)
Objective: manage vendors as part of the architecture — ensure SLAs, secure integrations, testability, and change governance.
Step-by-step
Vendor assessment & contracting — validate vendor capability, compliance certifications, SLAs for uptime, latency, data protection clauses, and change windows.
Define integration contracts — for each vendor produce API contract docs, data schema (ISO/JSON), message formats, error semantics, and test harnesses / sandboxes.
Connectivity & security — set up dedicated VPN/ExpressRoute links, mutual TLS, IP allowlists, and token/key rotation policies.
Operational runbooks — joint runbooks for incident handling, SLAs, escalation paths, DR procedures.
Test & certification — vendor sandbox integration, performance tests, security scans (third-party pentests if required), and compliance test scripts (e.g., sample CTR/STR flows).
Change governance — vendor change calendar, coordinated PI planning, and contractual change management process.
Integration patterns (examples)
Finacle/BaNCS: Real-time API for disbursement / ledger updates + nightly reconciliation batch.
Fenergo: API calls for KYC/CDD + webhook notifications for status.
Actimize: Batch SFTP ingestion + streamed alerts into Kafka for real-time monitoring.
Artifacts produced
Integration contract library
Vendor runbooks + SLAs
Certified sandbox tests & performance baselines
Stakeholders
Vendor Managers, Procurement, Legal, Security, EA, Ops.
KPI / Acceptance
Vendor SLA compliance %; integration test pass rate; time to remediate vendor incidents.
11. Top 30 Enterprise Risks (Sample 5 from each category)
Category | Risk Name | Risk Description | Owner | Mitigation Plan |
Business | Credit Default Surge | High NPAs if ML mispredicts PD | CRO | Dual ML models + stress testing |
Business | Vendor Lock-In | Dependence on Actimize/Fenergo | CIO | Multi-vendor strategy |
Business | Manual Review Bottleneck | Medium risk pile-up | COO | Auto-scaling underwriters |
Business | Regulatory Change | RBI new norms not met | Compliance | Flexible rule engine |
Business | Market Competition | Fintech agility | CEO | Invest in digital journeys |
Application | API Latency | External API slowdowns | CTO | Async retries, caching |
Application | Event Duplication | Duplicate Kafka events | App Lead | Idempotency keys |
Application | LOS Failure | Loan Orchestration crash | CTO | HA clustering |
Application | Document Upload Errors | Invalid Aadhaar/PAN OCR | App Lead | Validation services |
Application | Scaling Gaps | Peak traffic unhandled | CTO | Auto-scaling AKS |
Technology | AKS Outage | Cluster down | Infra Head | Multi-region AKS |
Technology | Kafka Partition Loss | Kafka cluster instability | CTO | Kafka MirrorMaker |
Technology | Redis Cache Eviction | Cache flush under load | Infra Head | Persistence enabled |
Technology | DB Latency | Postgres slow queries | DBA | Indexing, partitioning |
Technology | Cosmos Throughput | Exceeded RU | Data Lead | Auto-scale RUs |
Data | Data Drift | Feature store outdated | Data Science | Scheduled refresh |
Data | Wrong Feature Mapping | Misaligned features in ML | DS Lead | Data validation |
Data | Data Leakage | PII exposure in logs | DPO | Masking, tokenization |
Data | Inconsistent Consent | Consent mismatch | CDO | Immutable consent DB |
Data | ETL Failure | Actimize ingestion errors | Data Ops | Automated alerts |
Process | Loan Officer Delay | Manual disbursement delay | COO | SLA-based escalation |
Process | Audit Gaps | Missing audit trail | CISO | Immutable logs |
Process | Access Misuse | Excessive employee privileges | IAM Lead | SoD + SailPoint |
Process | Fraudulent Builder | Fake builder accounts | COO | Builder KYC workflow |
Process | Disbursal Misrouting | Funds sent to wrong account | Ops Lead | Dual validation |
Security | API Breach | Token replay | CISO | Short-lived tokens |
Security | Insider Threat | Employee fraud | CISO | Behavior monitoring |
Security | Ransomware | Infra encrypted | CIO | Backup + DR drills |
Security | SFTP Compromise | File tampering | CISO | SSH keys + checksum |
Security | Kafka Unauthorized | Rogue consumer | CISO | Kafka ACLs + TLS |
12. Full Lending Journey (Amit R)
Login → Angular UI → Azure AD → JWT token issued.
Loan Apply → Loan Service inserts into Loan DB + Outbox → emits LoanInitiated.
Parallel Checks → KYC (Fenergo), Credit Bureau (CIBIL), Fraud Vendor (Experian Hunter), AML/FinCrime (Actimize).
Events Emitted: KYC-Done, CreditScore-Done, FraudScore-Done, AML-Done, FinCrime-Done.
Loan Evaluation Service listens → emits Internal-ML-Score-Requested.
ML Service fetches features from Feature Store → returns PD, internal fraud score.
Loan Decision Engine applies rules (LTV, EMI affordability, regulatory caps).
Approve → Loan Agreement generated & eSigned.
Reject → Notification sent.
Medium → Manual underwriter review.
Loan Signed Event → Loan Account MS → TCS BANCS Adapter → Loan Account Created.
Builder Disbursement → Customer approval → Loan Officer validation → Saga triggers fund transfer to builder escrow account.
Repayment → EMI schedule generated in CBS → notifications to Amit R & Builder.
Compliance → Daily CBS → SFTP (signed, checksum) → Actimize ETL → CTR/STR/NTR/CBWR flagged → submitted to RBI/FIN-INS.
Identity Governance → SailPoint portal ensures only authorized staff can approve, review, and operate.
13 Digital Lending Journey – Layered Architecture View
Layer | Components / Actions | Details / Tech | Security / Governance |
Business | Loan Origination, Risk Evaluation, Compliance, Disbursement | Amit R applies for home loan; builder disbursement; EMI scheduling | Policies: Zero Trust, Segregation of Duties, SLA/KPI driven |
Business Capability → Service | Loan Application → LoanService MS Risk Evaluation → LoanEvaluation MS Disbursement → Disbursement MS Compliance → Compliance MS | Event-driven microservices, saga pattern, orchestration | Service-level access control, audit logs, SLA monitoring |
Application | KYC MS, CreditScore MS, FraudScore MS, AML MS, FinCrime MS, Internal ML Service, Loan Decision Engine, Loan Account MS | Spring Boot MS deployed on AKS; Angular UI frontend | Token validation, JWT, mTLS service-to-service, refresh tokens |
Data | Feature Store, Postgres BDR, Cosmos DB, Redis Cache, Azure Blob Storage | TDE at rest; Private Link; transactional & analytical data; audit logs | Role-based access, encryption at rest & in transit, secure metadata |
Technology | AKS, Kafka, Event Bus, Traffic Manager, Front Door, App Gateway, CI/CD Pipelines (Azure DevOps) | Event-driven flow, asynchronous processing, outbox pattern, saga pattern | Kafka TLS + ACLs + RBAC, monitoring via Prometheus/Grafana, ELK logs |
Security | Azure AD, SailPoint Identity Governance, JWT, mTLS, WAF, DDOS, Private Links | MFA login, token management, service authentication, Zero Trust | SailPoint access request & recertification, private links, policy enforcement |
Governance & Compliance | Fenergo, Actimize, RBI/FIN-INS reporting, Audit MS | Automated KYC/EDD/CDD, AML/CTR/NTR/STR/CBWR reports, monthly submissions, audit trail | Automated report generation & submission, DLQ for failed events, audit logging, access review |
Operational KPIs | Loan Processing Time, Loan Approval Rate, Compliance Accuracy, System Uptime, ML Accuracy | Current vs Modernized KPIs (TAT: 7 days → 1 day; Manual review reduced; Compliance errors ↓) | KPI monitoring dashboards, alerts, compliance SLA tracking |
Enterprise Risk Management | Business, Application, Data, Technology, Security, Compliance, Governance | Top 30 risks identified, assigned owners, mitigation plans, monitored continuously | Risk mitigation enforced via governance policies, audit & review cycles |
14 KPIs (Before → After Modernization)
Category | Current KPI | Modernized KPI Target |
Business | Loan approval TAT = 7 days | ≤ 1 day |
Business | Customer dropout = 25% | ≤ 5% |
Application | Uptime = 95% | ≥ 99.99% |
Application | Avg API latency = 1.2 sec | ≤ 200 ms |
Technology | Release cycle = quarterly | Weekly |
Data | Error rate in KYC data = 15% | ≤ 2% |
Security | Audit findings = 10 major/year | 0 major/year |
Compliance | Manual compliance filing = 70% | ≤ 5% |
Governance | IAM review = yearly | Continuous |
Operational | MTTR (recovery) = 12 hrs | ≤ 30 min |
15 Compliance & Reporting
KYC/CDD/EDD: Fenergo generates compliance reports
AML / Financial Crime: Actimize ETL from CBS flat files (daily), UDM ingestion, rule evaluation, CTR/NTR/STR/CBWR segregation
Regulatory Submission: RBI & FIN-INS portals, token-based authentication, acknowledgement captured
Audit & Logging: All events logged with correlation IDs, retries, DLQ for failed events
16. Security Across the Journey
Hop / Resource | Security Mechanism |
UI → API | Azure AD, JWT, MFA |
Service → Service | mTLS, token validation, refresh tokens, Zero Trust policy |
Kafka | TLS, ACLs, RBAC, topic-level security |
Data Storage | TDE, Private Link, RBAC, encrypted metadata |
SFTP / Flat Files | SSH key, Timestamp + Checksum + Digital Signature |
Identity Governance | SailPoint: Portal-based access request, approval, recertification, audit logs |
Network / Perimeter | WAF, DDOS, Traffic Manager, Front Door, App Gateway |
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