docs/PPT_STRUCTURE.md

# BankBot AI — Presentation Structure (12 Slides)

---

## Slide 1 — Title Slide
**BankBot AI**
*An AI-Native Financial Operating System*

Subtitle: Production-grade · Real-time · Intelligent

Visual: Dark glassmorphism background, animated gradient orb, tech badges

---

## Slide 2 — Problem Statement
**Traditional banking apps are passive. BankBot is intelligent.**

| Problem | BankBot Solution |
|---------|-----------------|
| Banks show data, not insights | AI-powered financial analysis |
| No real-time fraud intelligence | Live anomaly detection scoring |
| No personalized coaching | Financial health score + AI briefing |
| No scenario planning | What-If Simulator with 36-month projection |
| No behavioral analysis | Spending heatmap + pattern detection |

Visual: Side-by-side comparison screenshot

---

## Slide 3 — Solution Overview
**Your AI Financial Twin**

5 core capabilities:
1. Real-time AI chat with full financial context
2. Predictive balance forecasting (conservative/expected/optimistic)
3. Automated fraud detection (4-factor scoring)
4. Behavioral spending analysis (heatmap, patterns)
5. Interactive what-if financial simulator

Visual: Dashboard screenshot

---

## Slide 4 — System Architecture
**Production-Grade Full-Stack Architecture**

```
Next.js 14 Frontend
    ↕ HTTPS / WSS
Nginx (TLS + Rate Limiting)
    ↕
FastAPI Backend (33 routes)
    ↕              ↕              ↕
PostgreSQL 15   Redis 7      AI Engine
→ SQLite        → memory     OpenAI → Groq
  fallback        fallback   → Ollama → offline
```

Key point: **Every layer has a fallback — the system never fully fails**

---

## Slide 5 — AI Intelligence Engine
**4-Tier AI Fallback Chain**

```
Priority 1: OpenAI GPT-4o-mini    (fastest, most capable)
    ↓ if unavailable
Priority 2: Groq llama-3.3-70b    (free tier, very fast)
    ↓ if unavailable
Priority 3: Local Ollama llama3   (fully offline)
    ↓ if unavailable
Priority 4: Rule-based engine     (always available)
```

Context injected per message:
- Live account balances · Transaction history
- Financial goals · Investment portfolio
- Behavioral patterns · Health score

Visual: Chat page with streaming animation

---

## Slide 6 — Real-Time WebSocket Architecture
**Streaming AI + Live Updates**

```
Browser ──WS connect──► FastAPI
Browser ──{ type: "chat", message: "..." }──►
         ◄──{ type: "chat_start" }──
         ◄──{ type: "chat_chunk", content: "H" }──
         ◄──{ type: "chat_chunk", content: "er" }──
         ◄──{ type: "chat_end" }──
Browser ──{ type: "ping" }──► (every 25s)
         ◄──{ type: "pong" }──
```

Features:
- Character-by-character streaming
- Heartbeat every 25s
- Exponential backoff reconnect (1s → 2s → 4s → 30s max)
- HTTP fallback when WebSocket unavailable
- Prompt injection prevention (9 regex patterns)

---

## Slide 7 — Financial Intelligence
**AI-Powered Analytics**

- **Health Score** — 100-point composite (6 dimensions)
- **Spending Heatmap** — weekly activity patterns
- **Category Intelligence** — AI insights per spending category
- **Net Worth Timeline** — balance trajectory
- **Behavioral Analysis** — late-night spending, weekend patterns
- **Subscription Optimization** — detect unused subscriptions

Visual: Analytics page screenshot

---

## Slide 8 — Fraud Detection Algorithm
**Real-Time Anomaly Scoring**

```
Transaction → Score 4 factors:
  Amount spike    > 3.5x avg  → +40 pts
  Timing anomaly  11PM–4AM    → +25 pts
  Rapid-fire      < 3min gap  → +20 pts
  Duplicate       same+10min  → +30 pts

Score ≥ 30 → logged to fraud_logs
Score ≥ 50 → status: "flagged"
Score < 30 → status: "verified"
```

Visual: Fraud alert notification screenshot

---

## Slide 9 — Performance & Caching
**Cache-Aside Pattern with Auto-Fallback**

| Endpoint | Cold | Cached | TTL |
|----------|------|--------|-----|
| Dashboard | 65ms | 10ms | 2 min |
| AI Score | ~2s | 10ms | 10 min |
| AI Briefing | ~3s | 10ms | 1 hr |
| Transactions | 18ms | — | — |

Optimization: `Query.with_entities()` — column-only queries, no ORM hydration
Result: **32x speedup** (2.1s → 65ms)

Cache: Redis → in-memory fallback (automatic, zero config)

---

## Slide 10 — Database Design
**10-Table Normalized Schema**

Core tables:
- `users` — profile, personality, AI settings
- `accounts` — checking/savings/investment
- `transactions` — 300+ with categories, tags, emotion labels
- `goals` — target amounts, AI-generated plans
- `investments` — portfolio with AI risk analysis
- `subscriptions` — with AI usage detection
- `notifications` — typed alerts (fraud/insight/warning)
- `fraud_logs` — risk scores, details, status
- `ai_insights` — cached AI-generated content
- `analytics_snapshots` — daily financial snapshots

Fallback: PostgreSQL → SQLite (automatic, same ORM code)

---

## Slide 11 — Security & Observability
**Production-Grade Engineering**

Security:
- JWT (60min access + 7-day refresh rotation)
- bcrypt hashing (rounds=12, direct library)
- Rate limiting (120/min API, 10/min auth)
- Security headers (CSP, X-Frame-Options, etc.)
- Prompt injection prevention (9 patterns)
- CORS restricted to configured origins

Observability (`GET /api/metrics`):
- Request count, error rate, auth failures
- AI provider health (calls/errors/latency)
- Cache hit ratio
- Per-route timing (avg + max)
- Last 50 errors with timestamps

CI/CD: GitHub Actions (backend lint, frontend build, Docker smoke test)

---

## Slide 12 — Demo + Conclusion
**Live Demo**

Demo flow (5 min):
1. Login → Dashboard (65ms load)
2. AI Chat → WebSocket streaming
3. What-If Simulator → live sliders
4. Analytics → heatmap + radar
5. System Status → live metrics

**What makes this different:**
- Not a prototype — deployable to production today
- Every feature backed by real data (301 transactions)
- AI that knows your finances, not generic advice
- Resilient architecture that never fully fails
- Full observability — you can see it working

**Numbers:**
33 routes · 14 pages · 10 DB tables · 65ms dashboard · 4-tier AI fallback

---

## Presenter Notes

**Opening line:**
> "Most banking apps show you data. BankBot understands it."

**Closing line:**
> "This isn't a student project that happens to use an AI API. It's a production-grade system where AI is the core — every response is personalized, every insight is grounded in real data, and every layer has a fallback."

**If asked about deployment:**
> "The frontend is on Vercel, the backend on Render with managed PostgreSQL and Redis. The whole stack can also run locally with a single command — `run.bat` on Windows."

**If asked about the most impressive feature:**
> "The AI orchestration layer. It builds a personalized system prompt from the user's live database records, streams the response through whichever AI provider is available, and falls back gracefully through 4 levels. Very few projects implement this kind of resilient AI infrastructure."