Credit Card Fraud Detection Using ML & DL

Abstract

The ascent of the digital payments industry is accelerating as the global economy increasingly adopts online and card-based payment systems. This transition, however, brings with it an elevated risk of cyber threats and fraud, now more prevalent than ever before. For banks and financial institutions, bolstering the detection of credit card fraud is of utmost importance. Machine learning (ML) is revolutionizing this domain, making the identification of fraudulent activities both simpler and more effective. ML- powered fraud detection systems are adept at identifying patterns and halting irregular transactions. The hurdles faced in this area are significant: vast quantities of data are processed daily, with the vast majority of transactions (99.8%) being legitimate; the data, largely confidential, is not readily accessible; not all fraudulent activities are detected and reported; and fraudsters continually develop new strategies to outsmart the detection models. Machine learning algorithms are capable of pinpointing atypical credit card transactions and instances of fraud, ensuring that cardholders are not billed for purchases they did not make. These ML algorithms outperform traditional fraud detection systems, capable of discerning thousands of patterns within extensive datasets. Moreover, ML provides valuable insights into consumer behavior through the analysis of app usage, payment, and transaction patterns. The advantages of deploying machine learning in the fight against credit card fraud are manifold, including swifter detection, enhanced precision, and increased efficiency when dealing with large volumes of data. Key Words: Credit Card Frauds, Fraud Detection, Correlation matrix, principal components, Random Forest.