Authors : P. G. Nemade; Shraddha Ingole; Khushi Sharma; Radha Ghom; Asmita Nandane; Sejal Shinde

Volume/Issue : Volume 11 - 2026, Issue 2 - February

Google Scholar : https://tinyurl.com/yck7zmec

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DOI : https://doi.org/10.38124/ijisrt/26feb1354

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Abstract : In today’s digitally driven banking environment, secure and reliable financial transactions are essential for economic stability and user trust. However, the rapid growth of online banking services has also led to an increase in fraudulent activities, unauthorized access, and cyber threats. SafeGuard is an intelligent bank transaction security system designed to enhance protection and ensure secure financial operations using artificial intelligence techniques. The system employs machine learning algorithms to analyze transaction patterns, identify abnormal behavior, and detect potential fraud in real time. By examining factors such as transaction amount, frequency, time, location, and user behavior, SafeGuard provides adaptive and accurate risk assessment. The integration of data analytics, anomaly detection, and predictive modeling enables the system to continuously learn from historical transaction data and respond effectively to emerging threats. With a scalable architecture and automated decision-making mechanism, SafeGuard offers a reliable solution for strengthening banking security. This project aims to improve transaction safety, reduce financial fraud, and promote user confidence in digital banking systems through intelligent, technology-driven security measures.

Keywords : Banking Security, Artificial Intelligence, Machine Learning, Fraud Detection, Anomaly Detection, Secure Transactions, Financial Cybersecurity.

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