How to come up with a tech strategy and enterprise architecture for a sample banking use case using GenAI, while also anticipating and mitigating negative scenarios.

✅ 1. Pick a Use Case

Let’s take a sample banking use case:

💼 Use Case: "Intelligent Customer Query Resolution System"Customers interact via chat (web/app) to get answers on products, transactions, or policies using GenAI, instead of calling or waiting for an agent.

🎯 2. Define Objectives

• Reduce cost per customer interaction

• Improve CSAT (Customer Satisfaction) through instant answers

• Reduce load on human agents

• Maintain compliance, accuracy, audit trail

🧩 3. Identify Key Capabilities Needed

• Natural language understanding (GenAI)

• Secure customer identification & context awareness

• RAG pipeline with internal banking data (FAQs, policies, knowledge base)

• Audit logging and traceability

• Multi-channel integration (web, mobile)

• Feedback loop for model refinement

🧠 4. GenAI Strategy

Area	Strategy
LLM choice	Use Azure OpenAI or private LLM (like Mistral, LLaMA2) for data sovereignty
RAG layer	Index internal content (FAQs, policies, terms) using Chroma/FAISS, embeddings from OpenAI or HuggingFace
Data Chunking	Smart chunking with metadata (e.g., product, domain)
Prompt design	Use structured prompt templates, inject context, persona
Feedback loop	Capture thumbs up/down, route poor answers to training pipeline

🏛️ 5. Enterprise Architecture Overview

📌 Layers

1. Presentation Layer

• React Chat UI (with fallback to human)

• Mobile App integration

2. API Layer (Spring Boot)

• /chat, /feedback, /session

• Swagger / OpenAPI specs

• Handles request validation, rate limiting

3. GenAI Orchestration Layer

• Calls embedding store → retrieves chunks

• Prepares prompt

• Sends to LLM (OpenAI / Azure / On-prem model)

• Handles formatting, hallucination filters

4. Knowledge Layer (RAG)

• Vector DB (FAISS, Pinecone, Chroma)

• Ingests PDFs, webpages, KB articles

5. Security & Compliance Layer

• OAuth2 / OpenID for user auth

• Data masking, PII redaction

• Audit logging

• Response tagging (confidence, source, time)

6. Monitoring & Feedback Layer

• Prometheus + Grafana for metrics

• Model drift detection

• User feedback to retrain prompts or data

🚨 6. Handle Negative Scenarios (Risk Mitigation)

Negative Case	Solution
Hallucinated or incorrect responses	- Use RAG to ground responses - Add response disclaimers - Confidence scoring
Sensitive data leakage	- PII detection and masking - Only allow access to approved internal sources
Prompt injection attacks	- Sanitize input - Use guardrails (e.g., LangChain tools, Azure content filter)
Low confidence replies	- Route to live agent - Provide standard fallback response
Compliance & audit gaps	- Log every question/response - Store source documents used in response
Model drift over time	- Setup evaluation benchmark suite - Regular fine-tuning or re-training

🚀 7. Sample Tech Stack

Layer	Tools
UI	React, Tailwind, Chat UI
API	Spring Boot, Swagger, OAuth2
GenAI	LangChain, OpenAI/Azure, Prompt Layer
Vector Store	FAISS / Chroma / Pinecone
Docs	Apache Tika for parsing, LangChain ingestion
Security	OAuth2, Vault, WAF
CI/CD	GitHub Actions, Docker, Helm, ArgoCD
Infra	Kubernetes, EKS / AKS

🎯 Final Deliverables You Can Prepare

1. ✅ Architecture Diagram (C4 level: Context → Container → Component)

2. ✅ Tech Strategy Slide Deck (objectives, stack, risks, metrics)

3. ✅ PoC Plan (features, team, timelines)

4. ✅ Security & Compliance Checklist

5. ✅ Demo / Code Base with Spring Boot + RAG + Docker

✅ Slide: Next Steps – From Strategy to Execution

1. Executive Buy-In

• Align with Business Goals: Emphasize cost savings, improved CX, 24x7 support.

• Risk-Managed Adoption: Showcase controls for compliance, hallucination, and traceability.

• Cost Clarity: Provide initial PoC budget, infrastructure needs, and potential ROI.

• Champion Needed: Identify a business sponsor (e.g., Head of Digital, COO).

2. PoC Kickoff

• Objective: Demonstrate GenAI capability for resolving real customer queries.

• Scope: One channel (web), one product domain (e.g., savings accounts).

• Timeline: 4-6 weeks

• Team:

  • GenAI Architect (you)

  • Backend & RAG engineer

  • Frontend engineer

  • LLM & Prompt SME

• Success Criteria:

  • 60% query match rate

  • <5s response latency

  • High feedback score from internal testers

3. Architecture Alignment

• Governance: Work with security, data governance, and compliance teams

• Integration Points:

  • Identity (SSO, OAuth2)

  • Knowledge bases (existing KB, document store)

  • Observability (monitoring and logging)

• Scalability & Resilience: Validate containerization, auto-scaling, fallback routes

🔍 PoC Plan – Intelligent Customer Query Resolution

✅ Objective

Demonstrate feasibility of GenAI-powered RAG-based query resolution for banking customers through a secure, scalable architecture.

🔧 Key Features

• Web-based Chat UI (React)

• Spring Boot API Gateway

• GenAI RAG Layer (LangChain + Python)

• Vector DB integration (FAISS/Chroma)

• LLM APIs (Azure OpenAI / Open Source)

• Basic logging, audit, and fallback to escalation

👥 Team Composition

Role	Responsibility
GenAI Architect	Overall design, RAG strategy
Backend Developer	Spring Boot APIs, integration layer
Frontend Developer	Chat UI with feedback capture
DevOps Engineer	Docker, CI/CD setup, monitoring
Prompt/LLM Engineer	Prompt design, LLM integration, tuning
QA	Functional + Security Testing
Product SME (optional)	Query set creation, feedback validation

🗓️ Timeline (4–6 Weeks)

Week	Milestone
1	Environment setup, initial architecture, data prep
2	UI + API Gateway, initial vectorization pipeline
3	LLM integration, prompt design, RAG orchestration
4	Test flows, logging, feedback UI, fallback logic
5	Internal UAT, fine-tuning, test metrics
6	Final review, presentation to stakeholders

🔒 Security & Compliance Checklist

1. Data Security

•  PII Redaction: Ensure all personally identifiable information is masked or encrypted.

•  Data Encryption: In-transit and at-rest encryption for all sensitive data.

•  Access Control: Use role-based access control (RBAC) to restrict access to sensitive data.

•  Audit Logging: Enable logging for all user interactions with the system for audit and compliance purposes.

2. Privacy Compliance

•  GDPR Compliance: Ensure data handling practices meet GDPR requirements (e.g., user data deletion, consent management).

•  PCI DSS Compliance: If dealing with payment data, ensure compliance with PCI DSS standards.

•  Data Retention Policies: Define and implement data retention and deletion policies in line with compliance guidelines.

3. Model Governance

•  Transparency: Ensure GenAI model decisions are traceable, with source attribution for AI-driven responses.

•  Bias Monitoring: Implement checks to avoid and mitigate bias in GenAI responses.

•  Explainability: Ensure GenAI’s outputs are explainable, particularly in case of disputes or escalations.

4. Incident Response & Risk Management

•  Incident Response Plan: Develop a clear plan for handling security breaches or data incidents.

•  Escalation Flow: Define clear escalation paths for cases where the GenAI model is uncertain or encounters a risk.

•  Monitoring & Alerts: Set up real-time monitoring of the GenAI system to detect unusual behavior or security risks.

5. Regulatory Reporting

•  Compliance Reporting: Ensure systems are in place to generate compliance reports automatically.

•  Third-Party Audits: Engage with third-party auditors to validate the compliance and security posture of the system.

6. Third-Party Security

•  Vendor Risk Assessment: Perform due diligence and risk assessment for any third-party services used, such as cloud providers, LLM providers, etc.

•  API Security: Ensure API security (OAuth, rate-limiting, encryption).

7. Resilience & Continuity

•  Business Continuity Plan: Ensure the PoC has provisions for business continuity in case of system failure.

•  Disaster Recovery Plan: Implement a disaster recovery plan with defined RTO and RPO.

💡 Use Case: Intelligent Loan Eligibility Advisor

✅ Objective

Allow customers to interact with a GenAI-powered assistant to:

• Check eligibility for different loan products (home, personal, auto)

• Understand documentation requirements

• Get personalized suggestions based on basic profile inputs

⚙️ Architecture Stack

Layer	Tech
Frontend	React (Chat UI)
Backend Gateway	Spring Boot (REST APIs, Orchestration, Swagger, Logging)
GenAI Service	Python FastAPI RAG (OpenAI GPT/HuggingFace)
Vector DB	FAISS or ChromaDB
Data Source	Product PDFs, FAQs, policy docs
Containerization	Docker, Docker Compose
Authentication (optional)	Spring Security + OAuth2
Monitoring	Prometheus + Grafana or Spring Boot Actuator

🔁 User Flow

1. User enters basic details (age, income, city, profession)

2. Spring Boot forwards this input to RAG pipeline

3. Python RAG service searches the vector DB with semantic embedding

4. LLM answers with:

   • Eligibility response

   • Required documents

   • Next steps

5. Spring Boot formats & logs the interaction, returns result to React UI

📘 Sample Queries

• "Am I eligible for a personal loan with 5L income in Mumbai?"

• "What documents are needed for a self-employed home loan?"

• "Compare auto loan vs. personal loan for car purchase"

🔐 Negative Scenarios Handled

• Input sanitization and rejection of financial advice requests

• Fallback to safe responses like "please consult a loan advisor"

• Tracing and redaction of PII before passing to LLM