THE IMPACT OF MACHINE LEARNING ON FINANCIAL FRAUD DETECTION IN E-COMMERCE

  ABSTRACT: The fast boom of e-commerce has revolutionized the manner businesses function, providing purchasers with handy, efficient, and numerous alternatives for buying. However, this increase has also been observed by a great upward thrust in economic fraud, posing a first-rate danger to both organizations and purchasers. As online transactions grow to be extra everyday, fraudulent activities consisting of price fraud, account takeovers, identity robbery, and chargeback fraud have grow to be an increasing number of state-ofthe-art. Traditional strategies of fraud detection, regularly counting on manual checks and rule-based systems, have struggled to maintain pace with the scale and complexity of modern fraud schemes. As a end result, corporations and economic institutions are turning to superior technological solutions to combat fraud, with Machine Learning (ML) emerging as one of the maximum powerful and promising gear. Machine Learning offers a suite of state-of-the-art techniques capable of processing large volumes of transactional statistics in actual-time, permitting organizations to stumble on and prevent fraud with amazing accuracy. Unlike conventional systems that depend upon predefined rules and styles, ML algorithms can examine from historic records, perceive rising patterns of fraudulent activity, and adapt to new threats as they evolve. By analyzing various functions, which includes transaction quantity, region, charge technique, person behavior, and other relevant factors, ML models can verify the probability of fraud with a excessive degree of precision. These models continuously refine their predictions through the years, improving their capacity to stumble on even the maximum diffused and complex fraudulent activities. This study focuses on evaluating the transformative impact of machine learning on fraud detection in e-commerce.