Real-Time AI-Driven Fraud Detection and Prevention System for In-Person Transactions

An information-security system for detecting and preventing in-person bank fraud, comprising:

• a data input module configured to receive customer application details, including personal identification information, account numbers, transaction requests, and other relevant data;
• an artificial intelligence and machine learning (AI/ML) engine configured to analyze application data for inconsistencies, falsified information, or unusual requests by comparing the application data against a vast database of legitimate and fraudulent transactions to detect patterns such as mismatched information, unusually large transactions, or requests that deviate from customer typical banking behavior;
• a real-time conversation analysis engine equipped with speech recognition algorithms, configured to run on an associate's device to monitor live conversation between the bank associate and the customer, converting spoken dialogue into text for further analysis;
• a speech analysis module within the conversation analysis engine, configured to detect suspicious speech patterns, hesitations, inconsistencies in a story, high-pressure tactics, and keywords or phrases commonly associated with fraudulent activities, such as urgent requests for immediate action or reluctance to provide certain information;
• a risk assessment module configured to combine the analysis results from both the application data and the conversation to create a comprehensive risk assessment, ensuring that both verbal and non-verbal cues are considered to provide a holistic view of a potential fraud;
• an alert generation module configured to trigger an alert if a high probability of fraud is detected, wherein the alert is sent to the bank associate, security personnel, and other relevant individuals within the bank, including detailed information about reasons for suspicion to help staff make informed decisions about how to proceed;
• an action module configured to empower bank associates to take immediate action based on the alert, including verifying additional details with the customer, consulting with security personnel, or denying the transaction if necessary;
• a continuous learning module within the AI/ML engine, configured to update the system with new data and threat patterns, enhancing detection capabilities of the AI/ML engine by learning from each interaction to improve its accuracy and adapt to new fraud tactics;
• and
• a post-transaction monitoring module configured to monitor subsequent activities on the account if a transaction is flagged but allowed to proceed, tracking movement of funds, monitoring for unusual withdrawals, and analyzing further interactions with the bank, and alerting a security team if any additional suspicious activities are detected.

An information-security method for detecting and preventing in-person bank fraud, comprising the steps of:

• entering a customer's application details into an AI/ML engine;
• analyzing application data for inconsistencies, falsified information, or unusual requests using the AI/ML engine;
• simultaneously running a real-time conversation analysis engine on an associate device;
• converting spoken dialogue between an associate and the customer into text using speech recognition algorithms;
• analyzing conversation text for suspicious speech patterns, hesitations, or keywords often associated with scams;
• combining the analysis results from both the application data and the conversation to create a comprehensive risk assessment;
• triggering an alert if a high probability of fraud is detected, notifying the associate, security personnel, and other relevant individuals;
• empowering associates to take immediate action based on the alert to prevent fraudulent transactions;
• continuously updating a system with new data and threat patterns to enhance detection capabilities of the AI/ML engine;
• and
• monitoring subsequent activities on an account if a transaction is flagged but allowed to proceed, and alerting a security team if additional suspicious activities are detected.