Artificial Intelligence Based Fake or Fraud Phone Calls Detection

Abstract

Technology and fraud strategies have made fraudulent phone call detection more complex, from manual monitoring and basic rule-based systems to AI-driven solutions. Rule-based algorithms dominated fraud detection systems before AI. These algorithms identified previously false patterns. A rule may indicate calls from countries with high phone fraud rates or calls made to many recipients quickly. Static Blacklist Dependence Known fake numbers were tracked using static blacklists. These manually updated lists automatically blocked or flagged calls from these numbers. For instance, a phone number that consistently committed fraud would be blacklisted. Automatically block or review future calls from that number. Human Analysts Monitor Manually Because automated systems were limited, human analysts monitored and made decisions about possibly fraudulent calls. These analysts checked flagged calls for fraud. Analysts manually reviewed call records, listened to call recordings, and used their judgment and experience to uncover fraud. Caller ID and Basic Metadata Integration Limited. Current systems typically fail to recognize and prevent sophisticated fake/fraud phone calls, causing users huge financial losses and security breaches. These old methods can't handle modern fraud. By quickly and reliably detecting and mitigating fraudulent phone calls, AI-driven solutions improve user safety and confidence. Security and financial losses can be prevented by better detection. NLP and ML are used to analyze call patterns, voice features, and contextual data in real time in proposed systems. These systems detect and prevent fraudulent calls, reacting swiftly to new fraud strategies to safeguard users.