Abstract: Systems and processes are disclosed for a multi-layered approach to fraud prevention by leveraging a machine learning engine integrated with the Session Initiation Protocol (SIP) to attempt caller identification before transitioning to a voice call, allowing for the potential blocking of unwanted calls. If SIP-based identification remains inconclusive, an anomaly detection engine employing the Viterbi algorithm analyzes the caller’s speech patterns during voicemail messages. The Viterbi algorithm converts spoken language into text, identifying suspicious characteristics such as unusual speech patterns, inconsistencies, and keywords associated with scams. If suspicious characteristics are detected, the system automatically blocks callback attempts and notifies the customer of potential spam or unwanted calls. This proactive approach addresses both live and recorded fraudulent calls, enhancing the security of telecommunications by preventing fraudulent interactions before they can cause harm.

Abstract: Systems and processes are disclosed for a multi-layered approach to fraud prevention by leveraging a machine learning engine integrated with the Session Initiation Protocol (SIP) to attempt caller identification before transitioning to a voice call, allowing for the potential blocking of unwanted calls. If SIP-based identification remains inconclusive, an anomaly detection engine employing the Viterbi algorithm analyzes the caller's speech patterns during voicemail messages. The Viterbi algorithm converts spoken language into text, identifying suspicious characteristics such as unusual speech patterns, inconsistencies, and keywords associated with scams. If suspicious characteristics are detected, the system automatically blocks callback attempts and notifies the customer of potential spam or unwanted calls. This proactive approach addresses both live and recorded fraudulent calls, enhancing the security of telecommunications by preventing fraudulent interactions before they can cause harm.

Abstract: Systems and methods detect and prevent vishing attacks through an integrated framework combining SIP header customization, STIR/SHAKEN frameworks, AI/ML analysis, and real-time speech analysis using the Viterbi algorithm. The system begins with call initiation, embedding authentication information in the SIP header. The SIP data is transmitted and verified using STIR/SHAKEN frameworks, ensuring the authenticity of the caller's identity. Verified data is cross-referenced with third-party databases and analyzed by an AI/ML engine to detect anomalies. If potential fraud is detected, the call is blocked, and the customer is notified. Calls that pass initial checks are further analyzed using the Viterbi algorithm, which converts speech to text and identifies suspicious patterns. An anomaly pattern detector processes the converted text to detect vishing indicators, terminating the call if a match is found.

Abstract: Systems and processes are disclosed for real-time fraud detection and prevention in in-person transactions. The invention utilizes an AI/ML engine to analyze customer application data for inconsistencies and unusual requests indicative of potential fraud. Concurrently, a real-time conversation analysis engine with speech recognition algorithms monitors interactions between bank associates and customers, identifying suspicious speech patterns, hesitations, and keywords associated with scams. By combining insights from application data and conversational analysis, the system generates a comprehensive risk assessment. When a high probability of fraud is detected, an alert notifies the bank associate, security personnel, and other relevant individuals. This proactive approach enables immediate action to prevent fraudulent transactions, reducing manipulation risks and minimizing financial losses.

Abstract: Systems and processes are disclosed for enhancing cybersecurity and optimizing software repositories through integration of web crawling, web scraping, feature engineering, and advanced machine learning algorithms to detect phishing attempts, prevent account takeover fraud, and identify unused code in repositories. The system collects and refines data from various sources, including transaction logs, customer databases, device details, external data sources, and historical fraud data, to build comprehensive datasets. Feature engineering creates new, meaningful features from the refined data, which are used to train and evaluate machine learning models. The best-performing models are deployed in production to monitor incoming communications and transactions in real-time, flagging suspicious activities and optimizing codebases. This processing ensures timely detection and prevention of security threats while maintaining efficient software development processes.

Abstract: Systems and processes are disclosed for a multi-layered approach to fraud prevention by leveraging a machine learning engine integrated with the Session Initiation Protocol (SIP) to attempt caller identification before transitioning to a voice call, allowing for the potential blocking of unwanted calls. If SIP-based identification remains inconclusive, an anomaly detection engine employing the Viterbi algorithm analyzes the caller's speech patterns during voicemail messages. The Viterbi algorithm converts spoken language into text, identifying suspicious characteristics such as unusual speech patterns, inconsistencies, and keywords associated with scams. If suspicious characteristics are detected, the system automatically blocks callback attempts and notifies the customer of potential spam or unwanted calls. This proactive approach addresses both live and recorded fraudulent calls, enhancing the security of telecommunications by preventing fraudulent interactions before they can cause harm.

Abstract: Systems and methods detect and prevent vishing attacks through an integrated framework combining SIP header customization, STIR/SHAKEN frameworks, AI/ML analysis, and real-time speech analysis using the Viterbi algorithm. The system begins with call initiation, embedding authentication information in the SIP header. The SIP data is transmitted and verified using STIR/SHAKEN frameworks, ensuring the authenticity of the caller's identity. Verified data is cross-referenced with third-party databases and analyzed by an AI/ML engine to detect anomalies. If potential fraud is detected, the call is blocked, and the customer is notified. Calls that pass initial checks are further analyzed using the Viterbi algorithm, which converts speech to text and identifies suspicious patterns. An anomaly pattern detector processes the converted text to detect vishing indicators, terminating the call if a match is found.

Abstract: Systems and processes are disclosed for detecting phishing emails and text messages. The method involves accessing the internet to gather data from various online sources, executing multi-threaded downloaders to handle multiple data streams, and storing the downloaded data in a repository. A web scraping agent analyzes and extracts relevant features from the stored data, transforming unstructured data into a structured data model. Both are stored in a database. An after-processing dataset is generated, including testing and training datasets for machine learning analysis. Random Forest models are evaluated to determine accuracy in predicting phishing attempts, and optimal models are selected, which generate phishing predictions from new data, with feature extraction identifying attributes relevant for detection. An evaluation model assesses feature extraction accuracy and overall system performance. The machine learning algorithm adapts to new phishing techniques.

Abstract: A system for two-way projection of information and contactless communication of a data item includes a memory and a processor coupled to the memory. The processor initiates a virtual session for an interaction between an entity owner and a user relating to a set of objects and generates a non-fungible token for the virtual session. The processor tracks current information associated with the set of objects and simultaneously project the current information to a first virtual device of the entity owner and a second virtual device of the user. The processor receives, via the second virtual device of the user, a selection of one or more objects and a data item with respect to the selected objects. The processor communicates the data item and the generated non-fungible token for fulfillment of the selected objects to the user. The data item is communicated without requiring explicit contact by the user.