Natural Language Processing for Voice-Based Banking Interactions: Enhancing User Interaction Through Voice Commands

The following paper will outline every approach to voice-based banking with the help of NLP, voice recognition, and security features to improve financial services' usability. These include state-of-the-art methodologies like ASR or intent classification using BERT and others or context-aware models so that any type of interaction with the user is smooth and the user is engaged accordingly. Security is achieved using voice recognition and multiple-factor authentication (MFA) on sensitive transactions. More advanced fraud identification is done with the help of GANs and SVMs for real-time abnormality detection. Also, it works with multiple languages and has been updated since inputs are received and model calibration is performed. The proposed methodology involves assessment criteria related to performance parameters to get very low speech recognition errors and identify appropriate intent and fraudulence, particularly when banking services are availed through voice commands.