FinSecure: Financial Assistance and Transaction Fraud Detection System

Abstract

The Financial Assistance and Transaction Fraud Detection System is designed to address one of the most critical challenges in today’s digital economy ensuring security and transparency in financial transactions. The rapid digitization of financial services has revolutionized the way individuals and organizations manage money, offering ease of access and speed. However, it has also given rise to a surge in sophisticated fraudulent activities, such as fake claims, identity theft, and unauthorized transactions. These issues threaten the reliability of financial systems and lead to significant monetary losses for both institutions and users.

Introduction

With the rise of digital banking, e-commerce, and online payments, financial transactions have become faster and more accessible, but also more vulnerable to fraud, including identity theft, phishing, and unauthorized transactions. Traditional rule-based systems often fail to detect sophisticated fraud, suffer from high false positives, lack real-time monitoring, and provide generic financial assistance that ignores individual user behavior.

The FinSecure system addresses these limitations through an intelligent, multi-functional framework that combines machine learning, data analytics, and financial behavior modeling. Key components include:

1. Fraud Detection: Utilizes algorithms like Decision Trees, Random Forest, XGBoost, and Neural Networks, along with techniques such as SMOTE and cost-sensitive learning, to detect abnormal patterns in real time and adapt to evolving fraud tactics.

2. Financial Assistance: Provides personalized budgeting, saving, and investment recommendations based on transaction history and user behavior.

3. User Authentication: Ensures secure access through login credentials and encrypted data handling.

4. Transaction Management: Records, validates, and processes financial activities while preventing inconsistencies.

5. Alerts and Notifications: Sends real-time email alerts for suspicious transactions and financial advice.

6. Reporting and Analytics: Generates visual dashboards, transaction summaries, fraud trends, and financial insights for users and administrators.

The system integrates a responsive frontend (using frameworks like React or Streamlit), a secure backend (using Django, Flask, or FastAPI), and deployed machine learning models on cloud platforms with Docker and Kubernetes for scalability.

Results demonstrated that FinSecure effectively identifies fraudulent transactions, provides timely financial advice, ensures secure and smooth transaction processing, and offers interactive dashboards for real-time monitoring, significantly improving user trust, security, and financial management.

Conclusion

FinSecure successfully integrates secure authentication, real-time fraud detection, and personalized financial assistance into a single, user-friendly platform. The system addresses major challenges in digital finance by combining machine-learning models, encrypted data handling, and automated alerts to ensure that users receive immediate protection against suspicious activities. At the same time, the financial assistance module enhances user awareness by offering meaningful insights and recommendations based on transaction history. Through systematic design, rigorous testing, and effective module integration, FinSecure demonstrates that financial safety and user guidance can coexist within one intelligent framework. The project ultimately proves that combining security technologies with financial analytics can significantly improve user confidence, reduce fraud risks, and support better financial decision-making

References

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Copyright

Copyright © 2025 Divya Patil, Afreen G D, Anusha G S, Gousiya Asma I, Vidyashree V. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.