<ul><li>With rapid technological advances and increased internet use in business, cybersecurity has become a major global concern, especially in digital banking and payments.</li><li>Researchers have developed FinSafeNet, a deep-learning model for secure digital banking, which achieved 97.8% accuracy in fraud detection on the Paysim database.</li><li>FinSafeNet incorporates advanced features such as Bi-LSTM, CNN, dual attention mechanism, and optimized feature selection using the Improved Snow-Lion Optimization Algorithm (I-SLOA).</li><li>The model offers potential for real-time deployment in diverse banking environments, and future blockchain integration could further reinforce transaction security against cyber threats.</li></ul>

FinSafeNet: Advancing Digital Banking Security with Deep Learning for Fraud Detection and Real-Time Transaction Protection

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