Computer Vision for Financial Fraud Prevention using Visual Pattern Analysis

There has been an increase in the tactical ways for financial fraud due to the sophistication of their schemes. A concrete area where the established systems fail is where complexity of visio-inferencing is required, such as scams for scanned cheques, transaction heatmaps, and papers with personal presentation. The possible solving dimensions of the CV inception-amenable elements in the deep running are explored in the sense of a wider overview of financial fraud-viewing discernment. The CNN, attention model, and GAN are put together into multifaceted trading anomaly models suitable for static as well as moving images of financial repository. We tested the model over various test datasets of document forgery, fake currency, and inconsistent image characteristics displaying transaction behavior. Our model’s accuracy, precision, recall, and scores far exceed what is historically set by a bulk of normal ML methods, especially for the state-of-the-art models. We also discuss the trade-off of various CV architectures for various fraud detection modality settings. The findings also will underscore the potentiality of visual analytics bolstering digital financial systems in the face of emerging threats.