Data mining is the activity of extracting some useful knowledge from a large database using any of its methods. Due to a impetuous promotion in electronic commerce technology, in use from credit cards It has increased. As the credit card becomes the most popular payment method for both online and physical purchases, cases of credit card fraud are also on the rise. Data mining techniques can be used to detect credit card fraud. The main purpose of this article is to compare data mining methods such as simple K-means, hidden Markov model, Bayesian Net, KNN algorithm as well as ejection detection.

Payment cards have evolved from embossed paper tokens in the 1920s to today’s multilayered, data-rich, machine-readable artefacts. Their outward appearance—the printed design, dimension, embossing and embedded electronics—hides a precisely standardised internal logic. At the visual level, cards must be instantly recognisable, brand-aligned and resistant to wear; at the data level, the primary account number (PAN) and ancillary codes must satisfy global routing, security and regulatory requirements. This article integrates research from materials engineering, digital security and payment-network governance to explain (1) how the surface aesthetics and physical composition of cards are engineered, (2) how the numbering system encodes issuer, industry and account identifiers, and (3) how both layers interact to satisfy durability, interoperability and fraud-mitigation goals.