Fraud analytics is an essential tool for businesses to combat the increasing threat of online fraud by using data science and analytics to identify fraudulent transactions and behaviors in real time. These tools deploy machine learning and artificial intelligence to analyze vast amounts of data, enabling companies to detect inconsistencies and anticipate fraud before it occurs, thus protecting financial assets and customer trust. The global market for fraud detection and prevention is expected to grow significantly, with fraud analytics playing a crucial role in improving operational efficiency, reducing financial losses, and enhancing proactive risk management. By employing techniques such as descriptive, diagnostic, predictive, and prescriptive analytics, businesses can identify patterns, predict future fraud risks, and devise effective prevention strategies. Fraud analytics also support efforts to secure online transactions, prevent chargebacks, and protect against account takeovers and credit card fraud, ultimately ensuring long-term security and resilience against fraud.