AlgoBank: A High-Performance, Multi-Threaded Banking System in C++

Executive Summary

AlgoBank is an exemplary C++ project that demonstrates a strong, practical command of fundamental computer science principles. This project implements a multi-threaded, client-server architecture to simulate a real-world banking application. It showcases not only a deep understanding of various data structures and algorithms but also a thoughtful approach to system design, scalability, and modularity.

The core strength of this project lies in its justification of technology. Each feature is powered by a deliberately chosen data structure, and the rationale behind each choice is sound, reflecting an ability to map theoretical knowledge to practical problem-solving. The separation of the core banking logic from the network-facing server and the user-facing client demonstrates a mature understanding of software engineering principles like separation of concerns.

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🧠 Project Evolution

This is a complete rewrite of the original AlgoBank, which was a CLI-based single-user system.

Key improvements:

• ✅ Introduced TCP networking for multi-device communication
• ✅ Added multithreading to handle concurrent client requests
• ✅ Implemented server-side concurrency safety with mutexes

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System Architecture: A Scalable Client-Server Model

The application is architected as a robust client-server model, which is the industry standard for building scalable and distributed systems.

A high-level overview of the client-server interaction and the multi-threaded server design.

Architectural Breakdown:

• server (The Backend): This is the authoritative core of the system.

  • Concurrency: The server is multi-threaded, spawning a new thread for each client connection. This allows it to handle multiple clients simultaneously, which is essential for any real-world service.
  • Synchronization: Crucially, access to the shared BankSystem object is protected by a CRITICAL_SECTION (mutex). This is a vital demonstration of thread safety, preventing race conditions when multiple clients attempt to perform transactions or modify data concurrently.
  • Logic Core: It houses the BankSystem class, which encapsulates all business logic and data management.

• client (The Frontend): This is a lightweight, terminal-based user interface.

  • Thin Client: It contains no banking logic. Its sole responsibilities are to accept user input, format it into commands, send them to the server over a TCP socket, and display the server's response. This design makes the client easy to maintain and allows for different types of clients (e.g., a web or GUI client) to be developed in the future.

• Communication Protocol: Communication occurs over TCP sockets using a simple, text-based protocol (e.g., create <pin> <name>). The server processes these commands and returns a string response.

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Feature-Driven DSA Implementation: A Technical Analysis

The selection of data structures is purposeful and optimized for the task at hand, demonstrating a strong connection between theory and application.

Feature	Data Structure / Algorithm	Technical Rationale & Analysis
Concurrent Account Access	unordered_map, Mutex	Using a hash map for account lookup provides O(1) average complexity. The use of a mutex to serialize access ensures that operations like transfers are atomic, which is a correct and fundamental approach to concurrency control.
Financial Fraud Detection	DFS Cycle Detection on Graph	A cycle in a transaction graph (e.g., A -> B -> C -> A) is a classic pattern for fraud. Using DFS to detect cycles after each transfer is an elegant and computationally efficient way to flag suspicious activity in real-time.
High-Speed User Search	Trie (Prefix Tree)	For a feature like user search or autocomplete, a Trie is the optimal choice. It allows for prefix-based searches in O(L) time (where L is query length), which is significantly faster than iterating through all users.
Credit Risk Analysis	priority_queue (Min-Heap)	The admin dashboard needs to quickly identify the riskiest clients. A Min-Heap allows for efficiently querying the top k lowest-scoring users in O(k log n) time, which is far more efficient than sorting all users on each view.
Personalized User Caching	LRU (Least Recently Used) Cache	Caching recent payees improves performance and user experience. An LRU cache, implemented with a doubly-linked list and a hash map, is the classic solution, providing O(1) complexity for both put and get operations.
Social Network Analysis	Breadth-First Search (BFS)	To suggest "people you may know," the system finds users within a few "hops" in the transaction graph. BFS is the textbook algorithm for finding the shortest path in an unweighted graph, making it a perfect fit.
Transaction Community Detection	Disjoint Set Union (DSU)	The admin dashboard features an analysis of transaction "clusters." A DSU data structure is a highly efficient way to partition users into disjoint sets based on their transaction history, effectively identifying connected components.

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How to Build and Run

Prerequisites

• A C++11 (or newer) compatible compiler (g++, Clang, etc.)
• make (recommended)
• Windows: MinGW or Visual Studio C++ Build Tools
• Linux/macOS: build-essential or Xcode Command Line Tools

Compilation (Makefile Recommended)

Navigate to the project root and execute make:

make

This will produce two executables: server and client.

Execution

1. Start the Server: Open a terminal and run the server. It will begin listening for connections on port 8080.

   ./server
   # On Windows: .\server.exe

2. Start the Client: Open a second terminal and run the client to connect to the server. You can run multiple instances of the client.

   ./client
   # On Windows: .\client.exe

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Usage Demonstration

The system is initialized with five dummy accounts to allow for immediate testing.

• Account ID: 1001, Name: Alice
• Account ID: 1002, Name: Bob
• Account ID: 1003, Name: Charlie
• Account ID: 1004, Name: David
• Account ID: 1005, Name: Eve

Here is a sample walkthrough of the application:

1. Create a new account:

===================================
=== Welcome to AlgoBank System ===
===================================
1. Login
2. Create Account
3. Admin Dashboard
4. Exit
Enter your choice: 2

--- Create New Account ---
Enter your full name: John Doe
Create a 4-digit PIN: 9876

[SERVER RESPONSE]
Account created successfully for John Doe. Your Account ID is: 1006

2. Log in with the new account:

Enter your choice: 1

--- Login ---
Enter Account ID: 1006
Enter PIN: 9876

--- Welcome John Doe (Account #1006) ---
Balance: $1000.00
...

3. Transfer money and see a fraud alert: (First, transfer $100 from John (1006) to Alice (1001). Then, log in as Alice and transfer money back to John to create a cycle.)

--- Welcome John Doe (Account #1006) ---
Balance: $1000.00
...
Enter your choice: 1
Enter recipient Account ID: 1001
Enter amount: $100

[SERVER RESPONSE]
Transfer successful from 1006 to 1001.

(Now, in another client, log in as Alice and send money back to John to create a cycle)

--- Welcome Alice (Account #1001) ---
Balance: $1100.00
...
Enter your choice: 1
Enter recipient Account ID: 1006
Enter amount: $50

[SERVER RESPONSE]
Transfer successful from 1001 to 1006.

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
!!! FRAUD ALERT: A potential cycle of transactions has been detected starting from account 1001.
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!

4. Search for a user with the Trie:

--- Welcome John Doe (Account #1006) ---
...
Enter your choice: 3
Enter name prefix to search: Dav

[SERVER RESPONSE]
--- Search Results (Trie) ---
  - Name: David, Account ID: 1004

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Future Enhancements

This project provides a strong foundation. To evolve it into a production-grade system, the following areas could be addressed:

• Database Integration: The current system state is ephemeral. Integrating a database like SQLite or PostgreSQL would provide data persistence.
• Fine-Grained Concurrency: A single global mutex can be a bottleneck. A more advanced approach would be to implement fine-grained locking (e.g., a mutex per account) to increase throughput, while carefully managing potential deadlocks.
• API-Driven Design: The text-based protocol could be evolved into a standard RESTful API using a C++ web framework (like oat++ or Crow), decoupling the backend from a specific client implementation.
• Security Hardening: Implementing TLS/SSL encryption for all client-server traffic and performing rigorous server-side input sanitization are critical next steps for security.
• Robust Testing: Building out a suite of unit and integration tests (e.g., with Google Test) and setting up a CI/CD pipeline would automate testing and ensure code quality.