Information Systems and Mechanism for Prevention of Cyber Frauds

Cyber criminals are targeting online frauds, exploiting anonymity to deceive. They use fake websites, fake ads and stolen credit or debit cards for purchases. Bank frauds, despite high-speed processing and technical assistance, can damage a bank's reputation and operational efficiency, necessitating a strong emphasis on business ethics in the banking sector. The machine learning and artificial intelligence enhance online security of information systems. US enforces anti-trust laws and promotes stakeholder rights, addressing fraud and identity theft through safety tips and civil law implementation. Cyber criminals steal personal information for unauthorized purchases, identity theft, and fraudulent activities. Machine learning and artificial intelligence can enhance online security and user awareness. AI-based threat detection analyzes network activity for cyberattacks, limiting damage. Multi-Factor Authentication (MFA) combines traditional passwords with biometric authentication for enhanced security. The application of Big Data systems allows administrations for the collection and analysis of Data and its storage obtained from various sources, using platforms like Hadoop, Apache Spark, and Kafka for real-time processing and data analytics. Digital devices are increasingly being used in banking frauds, posing significant risks to both customers and the banking sector, necessitating stricter regulations and enforcement measures. Common banking sector issues include negligent, fraudulent, and deviant behavior, affecting various functions of getting, gathering, transporting, disbursing, loaning, trading, capitalizing, replacing, and servicing money-claims domestically and internationally. This paper discusses machine learning and anomaly detection techniques for preventing fraud in online payment systems, including behavioral profiling and Bagged Decision Tree models along with other methods.