C++ Smart Bank Integrated with AI Fraud Detection System

Overview

This project consists of two independent C++ programs working together:

1. Smart Banking System – Manages bank accounts, balances, and transactions, integrated with a MySQL database and a GUI.
2. AI Fraud Detection System – Analyzes transactions using machine learning to detect suspicious activities, such as unusual withdrawal patterns.

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How the Two Systems Work Together

• The banking system stores all transaction details in the MySQL database.
• The fraud detection system scans transactions regularly using anomaly detection techniques, analyzing factors like transaction frequency, location, and amounts.
• If fraudulent activity is detected, the system:
  1. Flags the transaction in the database.
  2. Sends a warning to the banking system, temporarily locking the affected account.

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Technologies Used

Banking System (C++ + Qt + MySQL)

• C++ with Qt – GUI for user interaction.
• MySQL – Stores user transactions and account details.
• C++ MySQL Connector – Facilitates database interaction.

AI Fraud Detection System (C++ + ML Library + Python Integration)

• C++ ML Libraries – Dlib, TensorFlow, or Scikit-learn (for advanced ML).
• Python (optional) – Used for training ML models (e.g., Scikit-learn).
• Alert/Email System – Notifies admins/customers about suspicious activity.

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Banking System Features

1. User Authentication

   • Secure login and registration.
   • Password hashing (using OpenSSL).

2. Account Management

   • Create, update, and manage accounts.
   • Deposit, withdraw, and check balances.

3. Transaction Logging

   • Every transaction is stored in MySQL.
   • Admins can monitor all transactions and flagged activities.

4. Fraud Detection Integration

   • Calls the fraud detection system.
   • Checks for flagged transactions and locks accounts if needed.

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AI Fraud Detection System Features

1. Transaction Retrieval

   • Fetches transaction data from MySQL for analysis.

2. Anomaly Detection

   • Uses machine learning to detect suspicious patterns.
   • Techniques: Isolation Forest, Neural Networks, Statistical Analysis.

3. Flagging Fraudulent Transactions

   • Updates the MySQL database when suspicious activity is detected.
   • Sends alerts to the banking system for action.

4. Continuous Model Training

   • Uses Python scripts to retrain the fraud detection model regularly.

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Project Deployment Details

Banking System (C++ + Qt + MySQL)

User Functions:

1. Create an account.
2. Withdraw money.
3. Deposit money.
4. View transaction history.

Technology Stack:

• Qt – GUI for user interaction.
• MySQL – Stores transactions and account details.
• C++ MySQL Connector – Communicates with the database.

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Fraud Detection System (C++ + ML + Python API)

Workflow:

1. Retrieves transaction data from MySQL.
2. Uses machine learning algorithms to detect anomalies.
3. Updates the MySQL database when fraud is detected.
4. Sends alerts to the banking system.

Technology Stack:

• C++ ML Libraries – Dlib, TensorFlow, or custom-built ML models.
• Python (optional) – If using Scikit-learn for advanced fraud detection.

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Integration of Both Systems

• The banking system continuously monitors fraud detection results.
• If fraud is detected:
  • The account is locked.
  • Alerts are sent to admins and users.

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Project Structure

1. Smart Banking System (C++ + Qt + MySQL)

/SmartBank  
│── src/  
│   │── main.cpp  
│   │── gui.cpp  
│   │── gui.h  
│   │── database.cpp  
│   │── transactions.h  
│   │── authentication.cpp  
│   │── authentication.h  
│   │── account.cpp  
│   │── account.h  
│   │── fraud_checker.cpp  
│   │── fraud_checker.h  
│── assets/  
│   │── bank_logo.png  
│── database/  
│   │── bank.sql  
│── config/  
│   │── db_config.ini  
│── README.md  
│── Makefile  

Key Modules:

1. Authentication (authentication.cpp)
   • Login/Register with hashed passwords (OpenSSL).
2. Account Management (account.cpp)
   • Deposit, withdraw, and balance check.
3. Transaction Handling (transaction.cpp)
   • Store and retrieve transactions from MySQL.
4. Fraud Detection Integration (fraud_checker.cpp)
   • Calls fraud detection API (if using Python).
   • Checks for flagged transactions.

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2. AI Fraud Detection System (C++ + ML/Python API)

/FraudDetection  
│── src/  
│   │── main.cpp  
│   │── fraud_detection.cpp  
│   │── fraud_detection.h  
│   │── db_connector.cpp  
│   │── db_connector.h  
│── models/  
│   │── fraud_model.dat  
│── scripts/  
│   │── train_model.py  
│── config/  
│   │── db_config.ini  
│── README.md  
│── Makefile  

Key Modules:

1. Database Connection (db_connector.cpp)
   • Connects to MySQL banking database.
   • Fetches transaction records for analysis.
2. Anomaly Detection Algorithm (fraud_detection.cpp)
   • Uses machine learning models for fraud detection.
   • Can integrate Python’s Isolation Forest algorithm.
3. Model Training (train_model.py)
   • Trains and improves fraud detection capabilities.
4. Transaction Flagging
   • Updates the MySQL database to mark suspicious transactions.
   • Sends alerts to the banking system.

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Integration Methods

1. Direct Integration (C++ only)

   • fraud_checker.cpp directly calls fraud_detection.cpp.
   • Uses a pre-trained model (models/fraud_model.dat).

2. API-Based Integration (Python ML Support)

   • Banking system sends transactions to a Python-based ML model.
   • Python analyzes transactions and returns fraud status.

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Conclusion

This C++ Smart Bank & AI Fraud Detection System combines traditional banking with AI-powered fraud detection. It demonstrates a real-world integration of two full-fledged C++ programs, offering security, scalability, and room for future improvements.