GenAI Use Cases in Pilot

🎯 Objective & Goals

“As an Enterprise Architect, I focused on identifying GenAI use cases that deliver measurable business outcomes — not just prototypes.

I led a Compliance Summarization initiative that cut review time by 80%.

Designed a GenAI FAQ Assistant reducing call-center load by 35%.

Built a Credit Assessment summarizer improving loan approval time by 70%.

Each solution followed enterprise architecture principles — decoupled microservices, Azure OpenAI for model serving, vectorized RAG pipeline, and a Responsible AI governance framework ensuring explainability, bias checks, and audit compliance.”

🧩 Use Case 1: Compliance Document Summarization & Intelligent Knowledge Hub

💡 Strategic Impact: Faster compliance reporting → Reduced regulatory penalties → 15–20% operational efficiency gain in compliance function.

Business Problem

ABC Bank’s compliance team spends hundreds of hours manually reviewing long policy, risk, and regulatory documents (RBI circulars, loan agreements, internal compliance reports). This leads to delays in regulatory submissions and inconsistent interpretation of clauses.

GenAI Solution

Implement a GenAI-powered Document Summarization & Knowledge Hub:

• Compliance documents auto-uploaded to internal repository.

• Documents chunked, vectorized, and stored in a semantic knowledge base (pgvector) with metadata.

• LLM (Azure OpenAI / internal Llama model) generates:

  • Executive summaries

  • Key obligations, risks, clauses, and action points

• A human-in-loop validation workflow ensures the compliance team can approve, reject, or correct summaries (feedback loop retrains model).

• Built-in Responsible AI guardrails: prompt validation, bias/hallucination detection, explainability audit, and role-based access.

Business Benefit

Metric	Before	After GenAI
Average document review time	4–6 hours	< 30 minutes
Compliance SLA breaches	~20%	< 5%
Manual rework effort	40%	< 10%
Knowledge retention (new employees ramp-up)	2 months	1 week

💬 Use Case 2: AI-Powered Customer FAQ Assistant (Loan & Product Advisory)

💡 Strategic Impact: Improved customer satisfaction and 24x7 digital engagement → Annual cost savings of ₹3–4 crore in support operations.

Business Problem

Customers frequently contact the bank for clarifications about loan terms, EMI options, and eligibility — burdening call centers and increasing turnaround time.

GenAI Solution

• Build a Retrieval-Augmented Generation (RAG) chatbot integrated with loan FAQs, policies, and T&C documents.

• Frontend via mobile app / website chat interface → backend using Spring Boot microservices + Azure OpenAI API.

• Queries are semantically searched through vector store (pgvector or Azure Cognitive Search) for relevant answers.

• The chatbot provides explainable, contextual responses — citing policy paragraphs.

• Responses are validated through a Prompt Governance Layer (prompt templates, validation, safety filters).

Business Benefit

Metric	Before	After GenAI
Call center volume	100% baseline	-35%
Average query resolution time	10 mins	<1 min
CSAT (Customer Satisfaction)	75%	92%
Cost per customer query	₹25	₹5

🧠 Use Case 3: Risk & Credit Assessment Summary Generator

💡 Strategic Impact: Accelerated credit approvals → Higher loan throughput → 20% increase in loan book capacity.

Business Problem

Credit risk analysts manually analyze borrower documents, balance sheets, and loan applications. This delays credit approvals and limits portfolio scalability.

GenAI Solution

• GenAI agent analyzes uploaded loan documents, financials, and bank statements.

• Uses RAG pipeline + internal credit policy data to summarize:

  • Borrower risk highlights

  • Key financial ratios

  • Covenant breaches

  • Suggested decision rationale

• Integrates with core lending system via REST APIs.

• Analysts get a “Credit Summary Sheet” auto-generated for review (human-in-loop validation).

• Responsible AI guardrails ensure transparency and audit trail of AI-generated summaries.

Business Benefit

Metric	Before	After GenAI
Credit analyst effort per case	3–4 hours	30 minutes
Loan decision turnaround	2–3 days	< 6 hours
Error rate in data interpretation	15%	< 3%

1️⃣ Intelligent Compliance & Risk Document Summarization

(Internal – Production-grade, low regulatory risk)

🧩 Use Case

Compliance and risk teams are overloaded with reading 1000s of pages — RBI circulars, policy updates, product T&Cs, KYC/AML documents, loan agreements, etc.GenAI automatically summarizes, extracts key clauses, flags risk areas, and creates an internal knowledge base searchable via natural language.

⚙️ How it works

• Documents uploaded to internal repo.

• Pipeline creates embeddings → stored in pgvector / Azure AI Search.

• Spring AI + Azure OpenAI generates summaries with key actions.

• Human-in-loop (compliance officer) validates and gives feedback for retraining.

• All actions logged under Responsible AI governance.

💼 Business Benefits

Area	Benefit
Compliance Efficiency	60–70% reduction in manual review effort.
Risk Mitigation	Fewer compliance breaches from missed clauses.
Audit Readiness	Every summary and model output traceable and explainable.
Productivity	Analysts can handle 3–4x more documents.

✅ Deployment Stage: Production (internal only, no PII exposure)

2️⃣ GenAI-powered Portfolio Advisor

(Customer-facing – controlled production rollout)

🧩 Use Case

Retail and HNI customers ask contextual questions about their investments:

“Why did my small-cap fund underperform this month?”“What’s my risk exposure if I add this new ELSS fund?”

GenAI provides explainable portfolio insights, grounded in:

• Customer’s portfolio data

• Internal research reports

• Product information (from RAG layer)

• Regulatory constraints (via prompt governance)

⚙️ Tech Flow

• Angular UI → Spring Boot + Spring AI → Azure OpenAI

• RAG fetches contextual financial data and research notes.

• Response validation & explainability layer ensures factual correctness.

• Audit log + bias validation built in.

💼 Business Benefits

Area	Benefit
Customer Experience	24x7 personalized portfolio insights.
RM Productivity	RMs focus on advice, not data compilation.
Engagement	25–30% higher digital engagement rate.
Upselling	Insights help identify cross-sell / up-sell opportunities.

✅ Deployment Stage: Limited production rollout with high-value customers (e.g., ICICI, DBS, JP Morgan Private Bank)

3️⃣ GenAI-driven Loan Document & FAQ Assistant

(Internal + customer-facing hybrid – pilot moving to production)

🧩 Use Case

Loan teams and customers spend huge time understanding loan documents, product FAQs, and agreements.GenAI provides:

• Summarization of key loan clauses for internal teams.

• Customer chatbot that answers loan-related queries (sourced from internal knowledge base).

• Responsible AI guardrails: factuality, hallucination control, and approval workflow.

⚙️ Tech Flow

• Documents & FAQs embedded into Azure AI Search / pgvector.

• Spring Boot microservices with Spring AI interface.

• Customer UI (web/chatbot) connects via secure APIs.

• Validation pipeline ensures compliance alignment (no speculative answers).

💼 Business Benefits

Area	Benefit
TAT Reduction	Loan officers save 50% time in document review.
Customer Support	40% fewer support calls; instant self-service.
Compliance	Summaries audited, factual, and traceable.
Scalability	Same GenAI engine reusable across products (personal loan, home loan, etc.).

✅ Deployment Stage: Pilot / limited production — safe domain (FAQs, static loan docs)