Modern economies rely on financial technology. Hence, the way in which provision of financial services is being changed by FinTech is an increasingly growing issue for security in financial transactions. This increasing reliance on technology further advances the need for security or protection in every financial transaction. The conventional ways of FinTech security are based on encryption and rule-based systems. These are not able to tackle more complex and dynamic threats at present. With rule-based systems being static, they tend to be vulnerable toward opening up new attack channels. It comes across as a serious threat to financial institutions and their customers. A dynamic and intelligent security framework needs to identify and counter this newly emerging threat at the earliest. The objective of this study is to present an improved LSTM-based deep learning method toward enhancing security in FinTech. The proposed LSTM-based deep learning system is dynamically adapted security for FinTech applications with dynamic updates regarding threats. The system identifies and can respond to new threats using this deep learning technique and ensure proactive defense against fraud and cyberattacks. We applied the proposed system in a FinTech simulation to determine its effectiveness over the traditional rule-based and machine learning-based security approaches. The results display an important increase in security with a 45% fraudulent transactions decrease. Comparing this model to that of a rule-based approach, deep learning-based is 85% more accurate and thus more responsive to emerging cyber threats. The outcome of the experiment reflects strong and proactive measures in deep learning for securities, reflecting the creation of a highly solid security framework for the FinTech world and contributing to increased resilience of financial systems in the digital age.