KYC / CDD / AML TM Solution

🏗 KYC / CDD / AML TM Solution Architecture (Text Flow)

[Customer Channel Layer]
    ├── Web / Mobile App
    ├── Branch Portal
    └── API Gateway (AuthN/AuthZ, Rate Limits)

↓  (REST/GraphQL API Calls)

[KYC Orchestration Service]
    ├── Create Correlation ID & Idempotency Key
    ├── Validate Input (Name, DOB, ID Docs)
    ├── Emit Event → [Topic: kyc.initiated]

↓  (Kafka Event Bus)

[KYC Microservice Consumers]
    ├── Document Verification Service
    │     ├─ OCR + Image Quality Check
    │     └─ Store results → Redis Cache + Cosmos DB
    │     └─ Emit Event → [Topic: kyc.doc.verified]

    ├── CKYC Integration Service
    │     ├─ Fetch CKYC record (if exists)
    │     └─ Emit Event → [Topic: kyc.ckyc.response]

    ├── Screening Service
    │     ├─ PEP/Sanctions Screening (OFAC/UN/EU lists)
    │     ├─ Adverse Media Check (Vendor API)
    │     └─ Emit Event → [Topic: kyc.screening.completed]

↓  (Events Aggregated in Orchestration Layer)

[KYC Decision Engine]
    ├── Apply Risk Rules (CDD Level: Low/Med/High)
    ├── Calculate Composite Risk Score
    └── Emit Event → [Topic: kyc.completed]

↓  

[Customer 360 / Golden Record Service]
    ├── Write final profile to Master Data Store
    ├── Maintain history (audit)
    └── Emit Event → [Topic: customer.onboarded]

↓  

[AML Transaction Monitoring Service]
    ├── Subscribe to [Topic: transactions.posted]
    ├── Apply AML Scenarios (structuring, smurfing, high-value transfers)
    ├── Generate Alerts → [Topic: aml.alert.generated]

↓  

[Case Management Service]
    ├── Create Case in Workflow Tool
    ├── Assign to Compliance Analyst
    └── Capture Analyst Disposition (True Positive / False Positive)

↓  

[Regulatory Reporting Service]
    ├── Aggregate confirmed alerts
    ├── Prepare STR/CTR/NTR as per FIU-IND schema
    └── Securely submit reports to regulator

↓  

[Monitoring & Audit Layer]
    ├── ELK / Prometheus / Grafana dashboards
    ├── Immutable Event Log (for regulator audit)
    └── Alerts on SLA breaches, failure patterns

[Customer Channels]

┌───────────────────────────────┐

│ Web/Mobile App / Branch UI │

└──────────────┬────────────────┘

│ (API Call)

▼

[API Gateway / Auth Layer]

│

▼

[KYC Orchestration Service]

(Creates Correlation ID, Idempotency Key,

Publishes → Topic: kyc.initiated)

│

─────────────────────────────────────────────────────────

Event Bus (Kafka / Azure Event Hub)

─────────────────────────────────────────────────────────

│ │ │

▼ ▼ ▼

[OCR & Validation Service] [CKYC Service] [PAN Verification Service]

- Extracts Name/DOB/ID - Query CKYC API - Call NSDL/IT Dept API

- Validates format/rules - Get CKYC record - Match PAN Name & DOB

- Fraud checks (tampering) - Store CKYC ID - Validate PAN status

- Emit: kyc.doc.verified - Emit: kyc.ckyc.resp - Emit: kyc.pan.verified

│

▼

[Aadhaar XML Service]

- Validate UIDAI digital signature

- Extract Name/DOB/Address/Photo

- Mask Aadhaar number (last 4 digits only)

- Emit: kyc.aadhaar.verified

─────────────────────────────────────────────────────────

▼

[KYC Decision Engine]

- Aggregate all responses (OCR, CKYC, PAN, Aadhaar)

- Apply risk rules (CDD Level: Low/Med/High)

- Generate Final KYC Status:

→ Approved / Rejected / Manual Review

- Emit Event: kyc.completed

─────────────────────────────────────────────────────────

▼

[Customer 360 / Master Data Store]

- Store Golden Record (with CKYC ID, PAN, Aadhaar Ref)

- Immutable Audit Logs (Data Lake / Blockchain Log)

─────────────────────────────────────────────────────────

Step 1 – Customer InitiationA customer submits their KYC documents via web, mobile, or branch channel. The request passes through an API Gateway where authentication, authorization, and rate limiting are applied. From there it goes into a KYC Orchestration Service, which creates a correlation ID and idempotency key to prevent duplicate processing. The orchestration service then publishes an event, for example kyc.initiated, onto Kafka or Azure Event Hub.

Step 2 – Parallel Microservices ProcessingDifferent microservices subscribe to this event and run in parallel.

• The OCR & Validation Service extracts fields like name, DOB, and ID number, performs format checks, and runs tampering or fraud detection. Once complete, it emits a kyc.doc.verified event.

• The CKYC Service integrates with the Central KYC Registry, searches for existing records, and returns a CKYC identifier, publishing kyc.ckyc.response.

• The PAN Verification Service connects to NSDL or Income Tax APIs, validates the PAN, matches the name and DOB, and emits kyc.pan.verified.

• The Aadhaar XML Service validates the UIDAI digital signature, extracts verified data, masks the Aadhaar number (showing only the last 4 digits), and publishes kyc.aadhaar.verified.

Step 3 – Aggregation and Risk AssessmentAll these results flow into the KYC Decision Engine. This service aggregates responses, applies business and compliance rules, and assigns a risk level — low, medium, or high CDD. It then decides whether the KYC is approved, rejected, or flagged for manual review, and emits a final kyc.completed event.

Step 4 – Customer 360 and AuditFinally, the Customer 360 Service writes the golden customer record into the master data store, along with the CKYC ID, PAN, and Aadhaar references. At the same time, all transactions and events are written to an immutable audit log or data lake to ensure regulator-ready compliance and end-to-end data lineage.

Summary

✅ Key Talking Points When Explaining This Flow

• Correlation ID + Idempotency: Guarantees duplicate prevention across async flow.

• Event-Driven: Decouples services, supports high-volume onboarding with low latency.

• Caching: Redis is used to avoid repeated CKYC/vendor calls, improving performance.

• Risk Scoring: Flexible rule engine + ML-based false positive reduction.

• Immutable Audit Logs: Every event is stored with timestamp → supports regulator inquiries.

• RegTech-Ready: FIU-IND integration automated, avoiding manual SAR/CTR uploads.

• Scalability: Microservices containerized on AKS/EKS/GKE with auto-scaling.

🔹 Q1. Onboarding Failures – CKYC / Aadhaar Downtime

Q: “Amit R’s CKYC fetch failed twice due to Aadhaar/UIDAI downtime, causing delays in his onboarding. How would you design the onboarding flow so that we don’t lose customers when CKYC is unavailable?”

✅ Model Answer:"I would design the onboarding as an event-driven, fault-tolerant flow with fallback options. The CKYC service would be exposed as a microservice integrated via API gateway. If Aadhaar/CKYC is down, the workflow would switch to alternative sources such as PAN NSDL APIs, DigiLocker Aadhaar XML, or Voter ID verification. To avoid multiple customer submissions, I’d use OCR and validation at source, cache results temporarily in Redis with a correlation ID, and once CKYC is back, reconcile automatically in the background. This ensures onboarding doesn’t break, audit logs are maintained, and regulatory compliance is met while the customer experience remains smooth."

🔹 Q2. AML False Positives – Balancing Compliance & Experience

Q: “Amit’s international remittance was wrongly flagged by AML TM. How will you balance between regulatory compliance and customer experience to reduce such false positives?”

✅ Model Answer:"Balancing compliance with customer experience requires a layered approach. First, I’d segment customers based on profile and behavior — for example, Amit’s regular salary credits and clean history mean lower risk. Then, I’d implement machine learning models on top of rules-based AML systems to re-score alerts, reducing false positives. Low-value, low-risk transactions could be auto-cleared, while high-risk patterns (structuring, unusual counterparties) stay prioritized. We’d also add an alert triage dashboard to route urgent cases to compliance teams in real-time. This way, regulatory obligations are honored, but genuine customers like Amit aren’t disrupted unnecessarily."

🔹 Q3. Regulator Escalation

Q: “Suppose the regulator reaches out after Amit posted on social media, asking for an explanation. How will you respond immediately, and how will you ensure such escalations don’t recur?”

✅ Model Answer:"My first step would be transparency. I’d provide the regulator with an incident report showing the root cause — CKYC unavailability and a false positive alert. I’d include audit logs proving that all customer data and compliance checks were intact, and detail how Amit’s case was remediated immediately. To avoid recurrence, I’d present a roadmap that includes fallback KYC APIs, machine learning–driven AML alert scoring, and customer communication improvements (like proactive notifications if a service is down). By showing both remediation and long-term improvements, we maintain regulator trust while improving resilience."

🔹 Q4. 90-Day Roadmap for Compliance Platforms

Q: “If you were leading delivery, what’s your 90-day roadmap to improve both KYC onboarding and AML TM effectiveness?”

✅ Model Answer:*"In the first 90 days, I’d focus on both quick wins and laying the foundation for modernization.

• First 30 days: Implement KYC fallback integration (PAN NSDL, DigiLocker), strengthen audit logs, and set up a triage team for AML false positives.

• Next 30 days: Deploy an alert prioritization dashboard, begin training ML models using historical AML alerts, and implement customer notification service for onboarding delays.

• Last 30 days: Pilot event-driven onboarding workflows using Kafka for CKYC/Aadhaar retries, integrate low-risk transaction auto-clear logic in AML, and establish regulator reporting templates.This phased roadmap delivers immediate relief to customers while progressing toward a scalable, resilient compliance architecture."*

🔹 Q5. Core Architecture Components

Q: “From a technical solutioning perspective, what core architectural components would you introduce to modernize compliance platforms?”

✅ Model Answer:*"I’d propose a microservices-based compliance platform, built on an event-driven backbone with Kafka for real-time streaming. Key components would include:

1. KYC/CDD Microservices – OCR & data validation, integration with CKYC, PAN, Aadhaar, DigiLocker.

2. AML Transaction Monitoring Layer – Rules engine + ML scoring models, connected to a graph database for relationship detection.

3. Audit & Logging Service – Immutable logs stored in Cosmos DB/Elastic for regulator-ready reporting.

4. Orchestration & Resilience – API Gateway + Service Mesh (Istio) with retry/fallback logic.

5. Customer Experience Layer – Real-time notification service and GenAI-based advisory assistant for proactive updates.This architecture ensures compliance, reduces false positives, and improves customer onboarding speed, while being regulator-friendly and future-ready."*

1. How would you design a scalable KYC solution for millions of customers while ensuring compliance?

Answer:I would use an event-driven microservices architecture with Kafka or Event Hub for decoupling and scalability. Each service handles one function — OCR, CKYC, PAN, Aadhaar, PEP/sanctions screening. The orchestration layer aggregates results. Data is stored in a golden record (Customer 360) with full audit lineage. This ensures regulatory compliance while being horizontally scalable for millions of customers.

2. How do you reduce false positives in AML transaction monitoring?

Answer:

• Dynamic rules tuning based on risk appetite.

• Machine Learning models (supervised or semi-supervised) to prioritize alerts.

• Customer segmentation — apply different thresholds for retail, corporate, HNI.

• Feedback loop from analysts to continuously refine models.This improves efficiency and reduces alert fatigue without missing true suspicious cases.

3. What are the key challenges in integrating with CKYC, PAN, and Aadhaar?

Answer:

• CKYC: Latency and downtime in CERSAI APIs → mitigate with retries and caching.

• PAN: Limited availability windows, name mismatches due to spelling issues → fuzzy matching helps.

• Aadhaar XML: Regulatory restrictions → must validate UIDAI signature, mask Aadhaar number, store only minimal fields.Overall, strong error handling, idempotency, and audit logging are critical.

4. How do you ensure end-to-end auditability for regulators like FIU-IND or SEBI?

Answer:

• Maintain immutable event logs with timestamp, correlation ID, and system user ID.

• Store all KYC & AML decisions in a data lake with versioning.

• Automate STR/CTR/NTR generation with lineage back to source data.

• Provide regulator dashboards with drill-down capability to each transaction.This ensures transparency and audit readiness.

5. How would you handle a scenario where CKYC APIs are down during onboarding?

Answer:

• Use graceful degradation: continue onboarding based on PAN, Aadhaar, and document OCR.

• Queue CKYC check asynchronously, mark profile as “provisional.”

• Once CKYC is available, reconcile results and update risk classification.This ensures customer experience is not blocked while compliance is still enforced.

6. How do you ensure data privacy while storing Aadhaar and PAN?

Answer:

• Aadhaar: Mask and store only last 4 digits, retain UIDAI XML signature for verification.

• PAN: Store securely with encryption (AES-256 at rest, TLS 1.2+ in transit).

• Apply data minimization principles — store only what is legally required.

• Use tokenization for sensitive IDs, and maintain RBAC + audit trails for access.

7. Can you explain how you would integrate sanctions and PEP screening into onboarding?

Answer:During onboarding, after OCR and registry checks, data is sent to screening services (e.g., Refinitiv, Dow Jones). The service runs name, DOB, country against PEP, Sanctions, and Adverse Media lists.

• Screening runs asynchronously and emits results back into orchestration.

• Alerts go into case management if flagged.

• Screening is also rerun periodically (daily batch or near real-time) to catch updates.

8. How would you manage regulatory reporting for suspicious activity (SAR/STR)?

Answer:

• Case Management Service escalates confirmed suspicious alerts.

• A Regulatory Reporting Service aggregates them into FIU-IND formats (STR, CTR, NTR).

• Reports are signed, encrypted, and transmitted securely.

• All submitted reports + acknowledgments are stored in immutable audit logs.This ensures compliance and evidence trail for audits.

9. What are the biggest risks in KYC/AML modernization projects, and how do you mitigate them?

Answer:

• False positives overload → ML + segmentation.

• Integration failures → retries, caching, async processing.

• Regulatory non-compliance → involve compliance team early, regular rule updates.

• Operational resistance → provide training and phased rollout.

• Data privacy violations → masking, tokenization, data retention policies.

10. How do you align technology delivery with compliance and operations teams?

Answer:I establish joint workshops with compliance, operations, and IT to define rules, risk appetite, and workflows. Build a compliance rule catalog mapped to technical scenarios. Use agile delivery with compliance sign-off in every sprint. Provide sandbox environments for ops teams to test workflows. This ensures alignment and adoption.