FinScore — Your Financial Health OS

Fragmentation

Users manage finances across multiple disconnected platforms:

• Banks
• Credit cards
• Brokerages
• Crypto wallets
• Subscription services

Lack of Predictive Insight

Existing tools focus on historical reporting:

• Credit scores look backward
• Budget apps summarize past spending
• Portfolio apps ignore cash flow

Limited Risk Awareness

Users cannot easily see:

• Emergency fund vulnerability
• Overexposure to volatile assets
• Cash flow instability
• Lifestyle inflation

Complexity

Financial analytics is difficult for non-experts to interpret, leading to poor decision-making.

Primary Users

• Digital professionals
• Freelancers
• Crypto-native investors
• Tech founders
• Remote workers
• Financial optimization enthusiasts

Secondary Users

• SMEs
• Startup employees
• High-income individuals
• DAO contributors

User Characteristics

• Comfortable with digital tools
• Multi-account users
• Moderate to high income
• Interested in financial independence

Core Functionality

• Secure account aggregation (bank, cards, crypto)
• Unified financial dashboard
• Dynamic financial health score
• Cash flow forecasting (30/90/180 days)
• Risk exposure analysis
• Alert system
• Goal tracking

MVP Scope Limits

• Read-only integrations
• Limited blockchains (Top 10)
• Basic scenario simulation
• Manual goal configuration

High-Level Architecture

Client Apps (Web/Mobile)
    ↓
API Gateway
    ↓
Auth Service ─── User Service
    ↓
Financial Aggregation Service
    ↓
AI Analytics Engine
    ↓
Scoring Engine
    ↓
Notification Service
    ↓
Reporting Engine

Components

• Frontend Layer (Web + Mobile)
• Authentication Service
• Data Aggregation Layer
• AI Processing Layer
• Scoring Layer
• Alert & Notification Layer
• Analytics & Reporting Layer

Architecture Style

• Microservices
• Event-driven processing
• Containerized workloads
• Cloud-native deployment

Users Table

users
- id (UUID)
- email
- password_hash
- kyc_status
- created_at

Accounts Table

accounts
- id
- user_id
- provider
- account_type
- balance
- last_sync

Transactions Table

transactions
- id
- account_id
- amount
- category
- timestamp

Assets Table

assets
- id
- user_id
- asset_type
- symbol
- quantity
- value_usd

Scores Table

scores
- id
- user_id
- liquidity_score
- risk_score
- stability_score
- overall_score
- generated_at

Alerts Table

alerts
- id
- user_id
- type
- message
- status

Authentication

POST   /auth/register
POST   /auth/login
POST   /auth/refresh

Accounts

GET    /accounts
POST   /accounts/connect
DELETE /accounts/{id}

Transactions

GET /transactions
GET /transactions/summary

Scoring

GET /score
GET /score/history
POST /score/recalculate

Forecasting

POST /forecast/simulate
GET  /forecast/results

Alerts

GET /alerts
POST /alerts/acknowledge

Frontend

• React / Next.js
• TailwindCSS
• React Query
• Chart.js / D3.js

Backend

• Node.js (NestJS)
• Python (AI Engine)
• FastAPI (ML APIs)

Data

• PostgreSQL
• Redis
• ClickHouse (analytics)

AI/ML

• PyTorch
• XGBoost
• Prophet (forecasting)
• LLM APIs

Infrastructure

• AWS / GCP
• Kubernetes
• Terraform
• GitHub Actions

End-to-end encryption

AES-256 at rest

TLS 1.3 in transit

OAuth2 integrations

Zero-trust architecture

Role-based access control

SOC2 compliance roadmap

Regular penetration testing

Subscription

• Free tier
• Pro (€12–20/month)
• Enterprise

API Licensing

• Fintech integrations

B2B

• White-label banking solutions

Premium Insights

• Advanced simulations
• Personalized advisory

Phase 1: Early Adopters

• Crypto communities
• Indie hackers
• Twitter/X founders

Phase 2: Content Marketing

• Financial blogs
• YouTube explainers
• SEO tools

Phase 3: Partnerships

• Neobanks
• Exchanges
• Payroll platforms

Phase 4: Enterprise Sales

• HR platforms
• Financial institutions

Pre-Build

• Landing page + waitlist
• Demo videos
• User interviews

MVP Testing

• Closed beta (100 users)
• Cohort analysis
• Retention tracking

KPIs

• Daily active users
• Score checks per day
• Churn
• LTV/CAC

Phase 1 (1 month)

• MVP build
• Core integrations
• Basic scoring

Phase 2 (1–2 months)

• Advanced AI
• More blockchains
• Mobile apps

Phase 3 (2–4 months)

• Open API
• B2B offering
• Compliance tooling

Phase 4 (4+ months)

• Financial identity layer
• Global expansion

• Regulatory compliance
• Data privacy laws
• API dependency risks
• Security breaches
• Model bias
• User trust barriers
• Scaling costs

• Credit underwriting
• Embedded lending
• DAO treasury tools
• Smart budgeting AI
• Automated tax optimization
• Insurance risk scoring
• Employer financial wellness tools

You are a senior full-stack engineer building the MVP for “FinScore” (Financial Health Operating System). Implement a production-ready but minimal system with a Next.js web app (user dashboard + admin ops), a NestJS (Node/TypeScript) backend API, and a Python FastAPI analytics/ML service for scoring, forecasting, and risk detection. Use PostgreSQL for transactional data, Redis for caching/queues, and S3-compatible object storage for exports/reports.
The system must securely aggregate financial data (banks, credit cards, brokerages, crypto wallets), normalize it into a unified ledger, compute a real-time forward-looking financial health score, provide cashflow forecasts and “what-if” simulations, and send proactive alerts with plain-language explanations.
GOALS (MVP)
User can sign up and authenticate (email magic link + Google OAuth acceptable as stub).
User can connect financial sources using sandbox integrations:
Banks / cards (Plaid-like sandbox or Open Banking stub)
Brokerages (CSV import stub acceptable)
Crypto wallets (read-only via address + chain selection; Top 10 chains)
System ingests and normalizes:
accounts & balances
transactions
assets (including crypto holdings)
liabilities (credit card balances, loans — can start with card balances)
System computes FinScore and sub-scores:
Liquidity
Stability
Risk
Resilience
Overall score (0–1000 or 0–100)
User can view a unified dashboard:
net worth + breakdown
cash flow (30/90/180)
category spending
risk exposure (e.g., crypto concentration)
score history + “what changed?”
User can run simulations:
income drop (percentage)
new monthly payment
emergency expense
market drawdown (portfolio drop)
System sends proactive alerts:
projected negative balance
unusual spending spike
high utilization / low liquidity
high volatility exposure
User can set goals:
emergency fund target
debt payoff target
savings target
Every score movement must have an explanation:
“Your score dropped 18 points because your cash buffer decreased and spending volatility increased.”
TECH STACK
Web App
Next.js (React), TypeScript
TailwindCSS
TanStack Query (react-query)
Zustand
Recharts (charts) + table/grid components
Backend API
NestJS + TypeScript
PostgreSQL (Prisma ORM)
Redis (BullMQ queues, caching)
JWT auth
OpenAPI/Swagger docs
Analytics / ML Service
Python FastAPI
Pandas / NumPy
Prophet (forecasting) or statsmodels baseline
XGBoost (optional for risk classification)
LLM APIs (explanations + summaries only; not for core math)
Integrations (MVP)
Banking sandbox (Plaid-like) OR mocked Open Banking provider
Crypto: EVM chains via RPC + token balances (or a provider API stub)
Optional: exchange read-only API keys (later; not required for MVP)
Infra
Docker Compose (local dev)
Deploy via AWS ECS Fargate / Fly.io / Render
S3-compatible storage for exports
GitHub Actions CI/CD
DELIVERABLES
A) Monorepo structure:
/apps/web
/apps/api
/apps/analytics
/packages/shared
B) Docker Compose running:
postgres
redis
api
analytics
C) Prisma migrations matching schema below.
D) REST API endpoints implemented with validation + auth + rate limiting.
E) Web UI flows:
Auth (magic link stub ok)
Connect accounts (sandbox + wallet address)
Dashboard (score + net worth + charts)
Transactions list + filters
Forecast + simulation builder
Alerts center
Goals page
Export report (PDF)
F) Seed script:
2 demo users
6 accounts
500 transactions
sample assets + crypto holdings
DATABASE SCHEMA (Prisma Models)
Core
User(id, email, authProvider, createdAt, kycStatus?)
Connection(id, userId, provider, status, accessTokenEnc?, refreshTokenEnc?, createdAt)
Account(id, userId, provider, accountType, currency, balance, lastSync)
Transaction(id, accountId, amount, currency, category, merchant, description, timestamp, pending)
Asset(id, userId, assetType, symbol, chain?, quantity, valueUsd, updatedAt)
Liability(id, userId, type, provider, balance, apr?, dueDate?, updatedAt)
Derived / Analytics
Score(id, userId, liquidity, stability, risk, resilience, overall, modelVersion, createdAt)
ScoreExplanation(id, scoreId, summary, factorsJson, createdAt)
Forecast(id, userId, horizonDays, inputsJson, outputsJson, createdAt)
Simulation(id, userId, scenarioType, paramsJson, resultJson, createdAt)
Alert(id, userId, type, severity, message, status, metaJson?, createdAt)
Goals / Reporting
Goal(id, userId, type, targetValue, currentValue, deadline?, createdAt, updatedAt)
Report(id, userId, type, objectKey, createdAt)
API ENDPOINTS (NestJS)
Auth
POST /v1/auth/login (magic link stub) → access+refresh
POST /v1/auth/refresh
GET /v1/me
Connections / Accounts
POST /v1/connections/bank/init (sandbox)
POST /v1/connections/bank/complete
POST /v1/connections/wallet { chain, address }
GET /v1/accounts
POST /v1/accounts/sync
Transactions
GET /v1/transactions?from&to&category&accountId
GET /v1/transactions/summary?from&to (totals by category + income/expense)
Assets / Liabilities
GET /v1/assets
GET /v1/liabilities
Scoring
GET /v1/score
GET /v1/score/history
POST /v1/score/recalculate
Forecasting / Simulation
POST /v1/forecast { horizonDays }
POST /v1/simulations { scenarioType, params }
GET /v1/simulations
Alerts
GET /v1/alerts
POST /v1/alerts/:id/ack
Goals
POST /v1/goals
GET /v1/goals
PUT /v1/goals/:id
Reporting
POST /v1/reports { type, range } → generates PDF, stores in S3, returns signed URL
ANALYTICS SERVICE (FastAPI)
Endpoints
POST /score
Input:
accounts, transactions, assets, liabilities
Output:
sub-scores + overall
factor breakdown (JSON)
explanation summary (LLM optional, using factors only)
POST /forecast
Input:
transactions history + balances + horizonDays
Output:
projected balance curve
confidence bands
risk of negative balance
POST /simulate
Input:
baseline snapshot
scenario type + params
Output:
deltas to forecast + score impact
“survival months” estimate
POST /alerts/generate
Input:
snapshot + forecast results + thresholds
Output:
alert list with severity + reasons
IMPLEMENTATION NOTES
Start rule-based, then ML: implement scoring with deterministic formulas first; keep modelVersion.
Canonical ledger: normalize all transactions and assets to a consistent internal format; store USD value snapshots.
Explainability required: store factor contributions (e.g., liquidity buffer, volatility, utilization) and generate explanations from those factors only.
Forecast baseline: use rolling cash flow averages + seasonality (Prophet optional).
Crypto valuation: for MVP, accept a price feed stub (daily prices) or inject from a provider; store valueUsd at sync time.
Queues: heavy tasks (sync, score, forecast) run async via BullMQ; API returns job IDs where needed.
Caching: cache dashboard aggregates and score for 1–6 hours; bust cache on sync completion.
Data retention: allow user delete/export; design for compliance (GDPR).
PROJECT STRUCTURE
/apps
  /web        (Next.js)
  /api        (NestJS)
  /analytics  (FastAPI)
/packages
  /shared     (types, zod schemas, scoring constants, category taxonomy)
LOCAL DEV
Provide docker-compose.yml with:
postgres
redis
api
analytics
Provide .env.example for each service.
Provide seed scripts with realistic transactions and assets.
DEPLOYMENT
Dockerfiles for api + analytics
Minimal deploy steps:
Postgres (RDS / Neon / Supabase)
Redis (Upstash / ElastiCache)
S3 bucket (private) for reports
ECS/Fly/Render services
Secrets Manager / platform secrets
HTTPS via Cloudflare / ALB
Add observability:
request logs
job queue metrics
error tracking (Sentry optional)
SECURITY CONSIDERATIONS
TLS 1.3 in transit, AES-256 at rest
Encrypt integration tokens (KMS or libsodium sealed boxes)
RBAC + least privilege (user vs admin ops)
Audit logs for access to financial connections
Rate limiting and abuse protection on auth + expensive endpoints
Signed URLs for report downloads
SOC2 readiness path:
access controls
logging
change management
incident response basics
QUALITY BAR
Zod/class-validator everywhere
Centralized error handling + typed error codes
Correlation IDs + structured logs
Unit tests:
scoring formulas
category mapping
forecast sanity checks
Integration tests:
connect → sync → score → dashboard
simulation impacts
Load testing for sync + analytics endpoints
SUCCESS METRICS
Time-to-first-score < 5 minutes (sandbox)
Score checks per week per user
Forecast usage rate
Alert open/ack rate
30-day retention
Conversion to Pro
Churn + LTV/CAC (post-launch)
FINAL INSTRUCTION
Now implement the FinScore MVP end-to-end following this specification.
Prioritize security, explainability, correctness of financial math, and reliability of integrations.