System requirements

Functional Requirements

Core Functionalities:

1. Account Management:
   • User registration, login, and account verification (e.g., KYC).
   • Link bank accounts, credit cards, or wallets to the user account.
   • View account balances, transaction history, and account settings.
2. Payment Processing:
   • Send payments to other users or merchants via email, phone, or account ID.
   • Receive payments from individuals or businesses.
   • Support recurring payments (e.g., subscriptions).
3. Fund Transfers:
   • Transfer funds between linked accounts (e.g., wallet to bank).
   • Handle currency conversions for international payments.
4. Fraud Detection and Protection:
   • Monitor transactions for suspicious activity using fraud detection algorithms.
   • Provide buyer and seller protection for disputes.
5. Multi-Currency Support:
   • Allow payments in multiple currencies with real-time currency conversion.
   • Display transaction breakdowns with exchange rates.
6. Notifications:
   • Notify users of transaction updates, failed payments, and security alerts.
7. Reporting and Analytics:
   • Provide users with detailed reports of their spending and income.

Non-Functional Requirements

1. Scalability:
   • Support millions of users and thousands of concurrent transactions per second.
   • Handle global traffic with low-latency performance.
2. Security:
   • Encrypt sensitive data (e.g., PCI DSS compliance for payment data).
   • Implement multi-factor authentication (MFA) and secure session management.
3. Reliability:
   • Achieve 99.99% uptime for payment processing services.
   • Use redundancy and failover mechanisms to prevent downtime.
4. Performance:
   • Process transactions in under 1 second for most cases.
   • Provide real-time balance and transaction updates.
5. Data Consistency:
   • Maintain strong consistency for account balances and transaction records.
6. Extensibility:
   • Enable easy integration with third-party services and APIs.
7. Monitoring and Auditing:
   • Log all transactions and activities for compliance and debugging.

Capacity estimation

Estimate the scale of the system you are going to design...

Assumptions:

1. Users:
   • Total registered users: 100 million.
   • Active users per day: 10 million.
   • Peak concurrent users: 1% of daily active users (100,000 users).
2. Transactions:
   • Average transactions per user per day: 5.
   • Total transactions per day: 10M×5=50M10M \times 5 = 50M10M×5=50M.
   • Peak transactions per second: 50M24×3600≈578 TPS\frac{50M}{24 \times 3600} \approx 578 \, \text{TPS}24×360050M​≈578TPS.
3. Storage:
   • Transaction size: ~500 bytes.
   • Total transactions stored yearly: 50M×365=18.25B50M \times 365 = 18.25B50M×365=18.25B.
   • Storage for transactions: 18.25B×500 bytes≈9.1 TB/year18.25B \times 500 \, \text{bytes} \approx 9.1 \, \text{TB/year} 18.25B×500bytes≈9.1TB/year.
4. APIs:
   • Peak API requests: 1,000 TPS during global usage spikes.

API design

Define what APIs are expected from the system...

1. Account Management APIs

• POST /api/users/register: Create a new user account.
• POST /api/users/login: Authenticate user credentials and issue a session token.
• PUT /api/users/update: Update user profile or account settings.
• POST /api/users/verify: Upload documents for KYC verification.

2. Payment APIs

• POST /api/payments/send: Initiate a payment to another user.
• POST /api/payments/request: Request payment from another user.
• GET /api/payments/{id}: Fetch the status of a payment.
• POST /api/payments/refund: Request a refund for a completed transaction.

3. Fund Transfer APIs

• POST /api/funds/transfer: Transfer funds between wallet and bank.
• GET /api/funds/history: View past fund transfers.
• POST /api/funds/convert: Perform currency conversion.

4. Fraud Detection APIs

• GET /api/fraud/check: Check if a transaction is flagged for fraud.
• POST /api/fraud/report: Report a fraudulent transaction.

5. Reporting APIs

• GET /api/reports/transactions: Generate a transaction report for a user.
• GET /api/reports/spending: Analyze spending trends for a user.

Database design

Defining the system data model early on will clarify how data will flow among different components of the system. Also you could draw an ER diagram using the diagramming tool to enhance your design...

1. User Database

• Schema Details:
  • Table Name: Users
    • user_id (Primary Key): Unique identifier for each user.
    • email: User email address.
    • phone_number: User phone number.
    • password_hash: Hashed password for authentication.
    • kyc_status: Verification status (e.g., pending, verified).
    • created_at: Timestamp for account creation.
• Purpose:
  • Store user account details and authentication information.
• Tech Used:
  • Relational Database (e.g., PostgreSQL, MySQL).
• Tradeoff:
  • Pros: ACID compliance ensures consistency for user-critical data.
  • Cons: Requires careful scaling for read-heavy operations.

2. Transactions Database

• Schema Details:
  • Table Name: Transactions
    • transaction_id (Primary Key): Unique identifier for each transaction.
    • sender_id: ID of the user initiating the transaction.
    • receiver_id: ID of the recipient.
    • amount: Transaction amount.
    • currency: Currency of the transaction.
    • status: Status of the transaction (e.g., pending, completed, failed).
    • created_at: Timestamp of the transaction.
• Purpose:
  • Log all transactions for record-keeping and auditing.
• Tech Used:
  • NoSQL Database (e.g., MongoDB, DynamoDB).
• Tradeoff:
  • Pros: Scales well for high write throughput.
  • Cons: Limited support for complex queries.

3. Fraud Detection Database

• Schema Details:
  • Table Name: FraudFlags
    • transaction_id (Primary Key): ID of the flagged transaction.
    • user_id: User involved in the flagged transaction.
    • reason: Reason for flagging.
    • review_status: Status of manual review.
    • created_at: Timestamp of the flag.
• Purpose:
  • Track potentially fraudulent transactions for review.
• Tech Used:
  • NoSQL Database (e.g., Cassandra).
• Tradeoff:
  • Pros: Low-latency access for real-time fraud checks.
  • Cons: Complex query support is limited.

4. Balance Database

• Schema Details:
  • Table Name: UserBalances
    • user_id (Primary Key): Unique identifier for the user.
    • balance: Current wallet balance.
    • currency: Currency of the balance.
    • last_updated: Timestamp of the last balance update.
• Purpose:
  • Track real-time wallet balances for all users.
• Tech Used:
  • Relational Database (e.g., PostgreSQL).
• Tradeoff:
  • Pros: Ensures strong consistency for critical balance updates.
  • Cons: High write throughput can require sharding.

5. Analytics Database

• Schema Details:
  • Table Name: SpendingTrends
    • user_id (Primary Key): User associated with the data.
    • spending_category: Category of spending (e.g., shopping, subscriptions).
    • amount: Total spending in the category.
    • period: Time period (e.g., monthly, yearly).
• Purpose:
  • Store aggregated data for reporting and analytics.
• Tech Used:
  • Columnar Database (e.g., Amazon Redshift).
• Tradeoff:
  • Pros: Optimized for analytical queries.
  • Cons: Inefficient for real-time updates.

High-level design

You should identify enough components that are needed to solve the actual problem from end to end. Also remember to draw a block diagram using the diagramming tool to augment your design. If you are unfamiliar with the tool, you can simply describe your design to the chat bot and ask it to generate a starter diagram for you to modify...

1. User Management Service

Overview:

• Manages user accounts, registration, authentication, and profile updates.
• Handles KYC (Know Your Customer) verification for compliance with financial regulations.

Responsibilities:

• Securely authenticate users and issue session tokens.
• Validate and store user information.
• Manage linked bank accounts and payment methods.

2. Payment Processing Service

Overview:

• Core service for sending and receiving payments.
• Manages payment initiation, authorization, and completion workflows.

Responsibilities:

• Validate sender and receiver accounts.
• Perform balance checks and update wallet balances.
• Handle multi-currency payments and conversions.

3. Fund Transfer Service

Overview:

• Handles transfers between wallets, banks, and external accounts.
• Supports withdrawals, deposits, and internal fund transfers.

Responsibilities:

• Validate transfer requests and initiate bank integrations.
• Track transfer statuses and update user balances accordingly.

4. Fraud Detection Service

Overview:

• Monitors transactions for suspicious activity in real-time.
• Flags high-risk transactions for manual review.

Responsibilities:

• Analyze transactions using fraud detection algorithms.
• Maintain a database of flagged transactions.
• Notify users of potential fraud and block risky transactions.

5. Notification Service

Overview:

• Sends transaction updates, payment confirmations, and alerts via email, SMS, or push notifications.

Responsibilities:

• Format and deliver notifications based on user preferences.
• Notify users of failed transactions, suspicious activity, or security changes.

6. Reporting and Analytics Service

Overview:

• Provides users and admins with transaction summaries and spending trends.
• Offers dashboards for business insights and compliance monitoring.

Responsibilities:

• Generate detailed reports on user activity and transaction trends.
• Support regulatory reporting for financial compliance.

7. Currency Exchange Service

Overview:

• Handles currency conversion for multi-currency transactions.
• Uses live exchange rates to calculate conversion costs.

Responsibilities:

• Fetch real-time exchange rates from external providers.
• Convert currencies during transactions with minimal delay.

8. Integration Layer

Overview:

• Connects the system with external financial services (e.g., banks, payment gateways).
• Provides APIs for third-party integrations.

Responsibilities:

• Sync transaction data with external systems.
• Ensure compliance with third-party API standards.

9. Admin Dashboard

Overview:

• Provides tools for administrators to monitor the system, resolve disputes, and manage flagged transactions.

Responsibilities:

• View transaction and fraud logs.
• Configure system settings and fraud detection rules.
• Manage customer support tickets and refunds.

Request flows

Explain how the request flows from end to end in your high level design. Also you could draw a sequence diagram using the diagramming tool to enhance your explanation...

1. User Registration Request

Objective: Register a new user and create an account.

Steps:

1. API Gateway:
   • Receives the POST /api/users/register request with user details.
   • Validates the request and forwards it to the User Management Service.
2. User Management Service:
   • Validates the input data (e.g., email, password).
   • Creates a new user in the User Database.
   • Sends a confirmation email to the user.
3. Response:
   • Confirms account creation or provides error details.

2. Payment Processing Request

Objective: Send a payment from one user to another.

Steps:

1. API Gateway:
   • Receives the POST /api/payments/send request with payment details.
   • Authenticates the sender's session and forwards the request to the Payment Processing Service.
2. Payment Processing Service:
   • Validates the sender and receiver accounts.
   • Checks the sender’s balance in the Balance Database.
   • Deducts the payment amount from the sender and credits it to the receiver.
   • Logs the transaction in the Transactions Database.
3. Fraud Detection Service:
   • Analyzes the transaction for anomalies.
   • Flags or approves the transaction based on risk assessment.
4. Notification Service:
   • Sends payment confirmation to both sender and receiver.
5. Response:
   • Confirms payment status or provides error details.

3. Fund Transfer Request

Objective: Transfer funds between a wallet and a linked bank account.

Steps:

1. API Gateway:
   • Receives the POST /api/funds/transfer request.
   • Authenticates the user and forwards the request to the Fund Transfer Service.
2. Fund Transfer Service:
   • Validates the request and checks the user’s balance.
   • Initiates the transfer with the Integration Layer.
   • Updates the transaction status in the Transactions Database.
3. Integration Layer:
   • Interacts with the external bank’s API to complete the transfer.
   • Monitors transfer success or failure.
4. Notification Service:
   • Notifies the user of transfer status.
5. Response:
   • Returns the transfer status to the user.

4. Fraud Alert Request

Objective: Flag a suspicious transaction for review.

Steps:

1. API Gateway:
   • Receives the POST /api/fraud/report request.
   • Forwards it to the Fraud Detection Service.
2. Fraud Detection Service:
   • Validates the transaction ID and marks it as flagged in the Fraud Detection Database.
   • Notifies the Admin Dashboard for review.
3. Response:
   • Confirms the transaction has been flagged.

5. Transaction Report Request

Objective: Generate a user’s transaction history.

Steps:

1. API Gateway:
   • Receives the GET /api/reports/transactions request with filters (e.g., date range).
   • Forwards it to the Reporting and Analytics Service.
2. Reporting and Analytics Service:
   • Queries the Transactions Database for matching records.
   • Formats the data for the user.
3. Response:
   • Returns the transaction report to the user.

Detailed component design

Dig deeper into 2-3 components and explain in detail how they work. For example, how well does each component scale? Any relevant algorithm or data structure you like to use for a component? Also you could draw a diagram using the diagramming tool to enhance your design...

1. User Management Service

End-to-End Working:

The User Management Service handles user registration, authentication, and profile updates. Upon receiving a registration request, the service validates input data (e.g., email format, password strength) and creates a user account in the database. It issues a session token after successful login. For KYC verification, users submit documents, which are processed asynchronously.

Data Structures/Algorithms:

• Hash Map for Session Management:
  • Stores session tokens mapped to user IDs for efficient authentication.
• Password Hashing:
  • Uses bcrypt or Argon2 for secure password storage.
• State Machine for KYC:
  • Tracks KYC status (e.g., pending, under review, verified).

Scaling for Peak Traffic:

• Horizontal Scaling:
  • Deploy multiple instances behind a load balancer.
• Caching:
  • Use Redis to cache frequently accessed user data.
• Read Replicas:
  • Scale read-heavy operations (e.g., user profile lookups) using database replicas.

Edge Cases:

• Duplicate Accounts:
  • Enforce unique constraints on email and phone number.
• Session Expiry:
  • Provide token refresh APIs to handle expired sessions securely.
• KYC Delays:
  • Notify users of processing times and provide real-time status updates.

2. Payment Processing Service

End-to-End Working:

The Payment Processing Service handles the entire lifecycle of a payment, including initiation, authorization, and completion. When a payment is initiated, the service verifies account balances and transaction limits. It then updates the sender’s and receiver’s balances in a transactional manner. Currency conversion is applied for international payments.

Data Structures/Algorithms:

• Transactional Ledger:
  • Implements double-entry bookkeeping to ensure consistent balance updates.
• Currency Conversion:
  • Uses a priority queue to fetch and apply the latest exchange rates.
• State Machine for Payments:
  • Tracks payment status (e.g., pending, authorized, completed, failed).

Scaling for Peak Traffic:

• Write-Optimized Storage:
  • Use sharded databases for high transaction throughput.
• Queue-Based Processing:
  • Offload non-critical tasks (e.g., email notifications) to asynchronous queues.
• Rate Limiting:
  • Implement transaction limits per user to prevent abuse.

Edge Cases:

• Insufficient Funds:
  • Reject the transaction with a detailed error message.
• Currency Conversion Failures:
  • Retry with the next available rate or notify the user of delays.
• Duplicate Payments:
  • Use idempotency tokens to ensure the same request is not processed twice.

3. Fund Transfer Service

End-to-End Working:

This service facilitates transfers between wallets, banks, and other external accounts. When a transfer is initiated, the service validates the user’s balance, processes the transfer via an external payment gateway, and updates transaction logs.

Data Structures/Algorithms:

• Directed Acyclic Graph (DAG):
  • Tracks dependencies in multi-hop fund transfers (e.g., wallet → bank → vendor).
• Retry Logic:
  • Uses exponential backoff to retry failed transfer attempts.

Scaling for Peak Traffic:

• Concurrent Connection Pools:
  • Maintain connection pools for frequent interactions with external banks.
• Batch Processing:
  • Batch fund transfer requests to optimize throughput during high traffic.

Edge Cases:

• Bank Downtime:
  • Queue transfer requests for retry when the bank’s system is restored.
• Transfer Reversals:
  • Automate reversal for failed or canceled transactions.

4. Fraud Detection Service

End-to-End Working:

The Fraud Detection Service evaluates transactions in real-time using machine learning models and predefined rules. It flags suspicious activity (e.g., unusually high transactions) and escalates flagged transactions for manual review.

Data Structures/Algorithms:

• Random Forest Classifier:
  • Analyzes transaction patterns to predict fraud likelihood.
• Sliding Window for Rate Analysis:
  • Tracks transaction rates over a rolling time window to detect anomalies.
• Blacklist/Whitelist System:
  • Maintains lists of known fraudulent or trusted entities.

Scaling for Peak Traffic:

• Stream Processing:
  • Use Apache Kafka for real-time ingestion and processing of transaction data.
• Feature Engineering Pipelines:
  • Pre-compute fraud detection features in parallel for high-throughput scoring.

Edge Cases:

• False Positives:
  • Allow users to appeal flagged transactions.
• Fraudulent Users:
  • Temporarily block accounts pending further investigation.

5. Notification Service

End-to-End Working:

This service sends real-time notifications for transaction updates, payment confirmations, and alerts. Notifications are formatted based on user preferences and delivered via email, SMS, or push notifications.

Data Structures/Algorithms:

• Priority Queue:
  • Prioritizes urgent notifications (e.g., failed transactions) over informational updates.
• Localization Cache:
  • Caches translated message templates for quick rendering.

Scaling for Peak Traffic:

• Message Queue:
  • Use RabbitMQ or AWS SQS to handle notification bursts.
• Bulk Delivery:
  • Batch notifications to optimize delivery during spikes.

Edge Cases:

• Delivery Failures:
  • Implement retries with exponential backoff.
• Message Duplication:
  • Use unique notification IDs to prevent duplicate deliveries.

Trade offs/Tech choices

Explain any trade offs you have made and why you made certain tech choices...

1. Relational vs. NoSQL Databases:
   • Trade-off: Relational for critical operations (e.g., balances) and NoSQL for logs.
   • Reason: Ensures strong consistency for balances and scalability for logs.
2. Event-Driven Architecture:
   • Trade-off: Added complexity for event management.
   • Reason: Enables decoupling and asynchronous processing for scalability.
3. Fraud Detection:
   • Trade-off: High false positives with strict rules vs. missed fraud with lenient rules.
   • Reason: Striking a balance reduces customer frustration while ensuring security.

Failure scenarios/bottlenecks

Try to discuss as many failure scenarios/bottlenecks as possible.

Transaction Delays:

• Issue: High traffic may cause processing delays.
• Mitigation: Use message queues and prioritize critical transactions.

Database Overload:

• Issue: High write operations overwhelm the database.
• Mitigation: Sharding and write-optimized storage.

Fraud Detection Latency:

• Issue: Real-time detection slows transaction processing.
• Mitigation: Pre-compute fraud features for faster scoring.

External API Failures:

• Issue: Bank or payment gateway downtime impacts transfers.
• Mitigation: Implement retries and graceful degradation mechanisms.

Future improvements

What are some future improvements you would make? How would you mitigate the failure scenario(s) you described above?

Enhanced Fraud Detection:

• Use deep learning models for more accurate predictions.
• Mitigation: Reduces false positives and improves fraud prevention.

Dynamic Scaling:

• Implement predictive autoscaling for peak traffic.
• Mitigation: Ensures consistent performance during surges.

Real-Time Analytics:

• Introduce streaming analytics for transaction monitoring.
• Mitigation: Enables faster response to anomalies.

Cross-Border Optimization:

• Partner with more local banks to reduce currency conversion fees.
• Mitigation: Improves user satisfaction for international transactions.