Machine Learning In Fraud Detection and Prevention

Abstract

Digital fraud has become a threat to businesses of all kinds. Under the IOT scenario, financial fraud refers to unauthorized mobile transactions using mobile platforms to fraudulently obtain funds through identity theft or credit card theft It has now become imperative for an organization to pay attention emphasize fraud and people suppression. Digitization has revolutionized the daily tasks we perform at the click of a button. In the real world, financial fraud detection under the IOT scenario requires advanced planning since financial fraud results in financial loss. Thus, we primarily analyzed financial fraud mechanisms using machine learning and deep learning techniques such as complex algorithms for fraud pattern detection. But fraudsters are also becoming smarter by the day and must be constantly monitored to prevent fraud and stay ahead of fraudsters. It is important to look at salient patterns that can help distinguish genuine behavior from fraudulent behavior. Client information such as Geo-location, authentication, session, and device IP address can be captured and monitored. The application of machine learning and artificial intelligence will play an ever-important role in detecting and identifying fraudulent patterns.