FRAUD DETECTION USING DATABASE ANALYTICS – CASE STUDIES ON DATABASE QUERIES AND AI FOR ANOMALY DETECTION

Database security requires fraud detection as an essential element because businesses and financial institutions keepincreasing their digital transaction use. Traditional fraud detection methods proved ineffective during assessmentsbecause contemporary fraudulent activities have become substantial and intricate schemes. Combining databaseanalytics with artificial intelligence (AI) operates as robust real-time anomaly detection techniques for dealing withthese security constraints. This research investigates the AI-analytics operational methods between database requestplatforms to perform real-time fraud detection through prior pattern analysis and future anomaly monitoring.Organizations attain maximum efficiency and minimum false-positive errors by combining structured databaserequests with AI-based predictive model analysis.The research investigates actual fraud identification platforms through database analysis and studies financialinstitution payment systems alongside Internet sales systems and insurance scheme operations. An evaluative studyanalyzes how SQL anomaly detection plans utilize machine learning systems to detect suspicious fraudulent datapoints. Standard rule-based fraud detection systems cannot recognize anomalies since artificial intelligence modelsanalyze data transactions for this purpose. AI fraud detection systems deliver excellent performance to businesses infinancial breach detection by enabling risk prevention measures.The evaluation investigates the main approaches that combine database queries with AI-based methods for anomalydetection. The detection of fraudulent behavior achieves improvement through the combination of machine learningtechniques neu,ral networks clu, steering algorithms, and decision trees. According to research results, companies thatimplement AI fraud detection algorithms decrease financial losses, improve detection, and produce fewer wrong alerts.Processors of digital transactions can build better security through continuous learning models because these systemsadapt to the changing fraud methods.This research focuses on the role of real-time fraud monitoring in improving business response times. By enablingAI-based anomaly detection systems, organizations can significantly enhance their ability to respond to potentialthreats. The combination of financial institutions and e-commerce platforms, for instance, can use automated alertsystems to prevent unauthorized transactions from reaching critical security breach points. This emphasis on efficiencyshould make the audience feel empowered by the capabilities of AI-based systems.This study reveals that businesses mandate adaptive defense strategies to fight fraud while continuing to develop newcybersecurity threats. Organizations require perpetual enhancement of their fraud prevention models becausecriminals advance their sophisticated fraud methods. The study proves that AI-integrated database analytics operatesas an adaptive process that delivers businesses total security instruments to combat fraud.This document shows that AI database analytics technology has become the core element for protecting detectionsystems from fraud activities. Organizations obtain exact counterfeit activity identification through their deploymentof AI-based anomaly detection systems that integrate structured database queries. Current digital economy securitydemands every business utilize AI-based fraud detection systems, which strengthen cybersecurity protection. Moderndigital business operations will be safer against emerging cyber threats because AI-powered fraud detectioncapabilities must be installed in all enterprises.