Economic systems frequently face the problem of counterfeit money circulation, which compromises financial credibility and transaction integrity. Even while manual and UV-based inspection methods are widely utilized, they are time-consuming, prone to human mistake, and inefficient and subjective, hence automated replacements are required. In order to improve and automate the detection of counterfeit cash, this study proposes a hybrid deep learning model that makes use of Transformers, Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs).

RNNs analyze sequential data, such money serial numbers, for anomalies, Transformers hone spatial correlations, and CNNs collect complex visual aspects. This multi-modal method facilitates real-time operation and increases detection robustness. Indian banknotes in values of 100, 200, and 500 Indian rupees are used to train the model.

Once a note is classified as genuine, the system integrates a real-time currency conversion module to convert its value into various foreign currencies, enhancing its practical applicability for financial and travel-related use cases. The model is implemented using TensorFlow and PyTorch, with optimization techniques including batch normalization, dropout, and transfer learning.

With little retraining, the system is made to be flexible and expandable across several money kinds. It may be implemented in settings including retail points of sale, ATMs, and banks. Additionally, the architecture is appropriate for mobile platforms and embedded devices because to its support for real-time processing. Its modular architecture also makes it possible to integrate OCR for serial number recognition in the future. All things considered, this approach offers a strong, effective, and expandable answer to the cash authentication issue.