Voice Authentication for Secure Banking

Contactless authentication using voice recognition to improve security, convenience, and cross-platform compatibility compared to conventional authentication methods that require contact. The authentication is performed by converting an audio sample from a user device into a processed format and transmitting it to a speech recognition module. The speech recognition module compares the processed audio against user voice models to determine if it matches. If the match is successful, the user is authenticated. This allows contactless voice-based authentication that can be used on any device with a microphone and speaker, without requiring dedicated authentication devices or manual entry.

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Secure electronic financial transactions system that uses AI, biometrics, and multimodal data analysis to prevent fraud and improve user experience. The system collects multimodal data from visual, auditory, and tactile sensors during transactions. It uses AI modules like biometric authentication, transaction anomaly detection, geospatial analysis, behavioral analysis, and third-party data integration to analyze this data for fraud prevention. The system also communicates with users through modalities like vision, audio, touch, taste, smell, temperature, pain, and balance to address fraud concerns or request verification.

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Automated offline payment system that allows users to select products without manually scanning them and makes payment without using their own devices. The system uses biometric authentication to verify users and their devices. When a user selects items in a store, a dedicated terminal recognizes them. The store terminal sends biometric info to the user's device. It authenticates against stored data. If successful, the device makes payment. Leaving the store triggers payment. In product transportation units, the dedicated terminal recognizes items and requests payment. It receives biometrics from the user's device and authenticates. If successful, it pays.

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Contactless authentication system using voice recognition that allows user devices to authenticate without physical contact or dedicated authentication devices. It involves receiving user voice data, converting it to a standard format, sending it to a remote server for voice matching, and using the match result to authenticate the user. This allows cross-platform compatibility since the authentication is performed remotely and doesn't require device-specific formats.

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Secure and efficient order pickup system that uses voice authentication to verify user identity and prevent unauthorized pickups. The system associates a voice signature with each order during placement. When a user comes to pick up an order, they speak a phrase which is compared against the stored voice signature. If it matches, the user is authorized to retrieve the order. This eliminates the need for IDs or documents, improves security, and reduces errors compared to manual verification.

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Contact-minimized automated teller machine (ATM) using radar-based gesture recognition and authentication to eliminate the need for physical cards and keypads. The ATM has a radar transmitter to create a field in front of it. Customers interact with the ATM using gestures instead of touching buttons or cards. The radar detects the gestures and translates them into actions. The ATM can also identify customers based on unique gestures learned during account setup. This provides contactless ATM usage and reduces transmission of germs.

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Improving authentication security by generating and processing user responses to authentication questions in a narrative format. The responses are textual inputs like "I shopped at Joe's last week" instead of multiple choice options. The text is processed to identify merchants and compare to transaction data. A machine learning model predicts guessability based on merchant popularity. If a user correctly identifies a matched merchant, the authentication score is determined based on guessability. This provides more secure authentication by preventing guessing and profiling from predefined options.

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Enabling users to schedule payments using voice or text commands through a payment app, without needing to navigate complex menus or remember OTPs. The app extracts payment instructions from natural language input and authenticates the user based on factors like location, facial features, typing patterns, etc. This allows scheduling payments using conversational commands like "Pay my credit card bill on Thursday" or "Send $50 to Mom next week". The app encrypts the instruction and factors and sends to the server, which processes the scheduled payment. This provides an intuitive and convenient way to schedule payments using natural language input and automatic authentication.

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Using machine learning to improve user authentication in financial transactions by generating more effective challenge questions that are tailored to the specific transactions of an authorized user. The method involves training a machine learning model to identify financial transactions that an authorized user is likely to remember based on their transaction history. This allows generating challenge questions about recent transactions that are more likely to be answered correctly by the authorized user.

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Quickly realizing highly secure voice authentication without requiring learning a global voiceprint model, using a calculation method like the Viterbi algorithm, or calculating an average reliability value. The method involves extracting a voice feature vector from a user's registered voice, generating a temporary password, presenting it to the user, receiving their voice reading the password, and authenticating using both voice feature vectors and the password. This allows fast voice authentication without complex speech recognition processing.

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Privacy-protecting voiceprint authentication to prevent exposure of user biometric data in voice authentication systems. The method involves synthesizing a voiceprint template by combining extracted voice features from a user's voice spoken in multiple modes, rather than sending the actual voice. This synthesized template is used for authentication instead of the original voice. This prevents exposing the user's actual voice during registration and authentication. The synthesized voiceprint template is sent to the server for storage, which cannot be reversed to obtain the user's original voice.

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Enhancing user authentication using a smart device like a smart speaker to improve security and reliability. The method involves using transaction-based authentication questions like "What was your credit card purchase on May 12?" instead of static questions. The user's voice is analyzed during the responses to determine if it matches an expected voiceprint. If so, fewer questions are needed for authentication. If not, more questions are required. This combines voice analysis with dynamic transaction-based questions to prevent spoofing of prerecorded voices.

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Voice verification method and network equipment that enhances the accuracy and security of voice password verification while also providing real-time performance. The method involves using a voice recognition server to compare a user's live voice input against a stored voice print, instead of using pre-recorded voice files. The live comparison reduces the risk of stolen usernames and forgotten passwords. The voice recognition server extracts speech features from the live input and compares them against the stored voice print in real-time. This avoids issues with voice details changing due to emotion or state differences between password setting and verification.

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Multi-factor authentication system that includes voice verification as a factor. The system allows users to enroll their voice for authentication by providing a few sample utterances. It trains a text-independent voice model using the samples to capture distinctive aural characteristics of the user's voice. During authentication, the system compares the user's live voice to the trained model to verify their identity. This provides an additional factor of authentication beyond passwords that is more convenient than using hardware tokens or biometrics.

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Exchanging data for secure transactions over voice channels to enable voice-based payment and other transactions. The method involves monitoring voice conversations for transaction requests, extracting details from the voice, confirming with the user, and carrying out the transaction using the extracted data. It uses voice encoding to embed transaction requests in audio and voice decoding to extract them. This allows performing transactions without separate channels or devices.

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Continuously authenticating a person by voice to extract their voice from a mixed audio stream or continuously monitor if an ongoing call is with a specific person. The technique involves continuously performing voiceprint authentication on the acquired voice in time series using stored voiceprint data. Authentication result values representing estimation degrees are generated. Thresholds are used to selectively pass or discard the voice based on how closely it matches the target. This allows extracting just the target person's voice from a mixed stream or continuously monitoring if a call is with the target.

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Enhanced security for accessing resources like bank accounts using biometric verification at the time of transaction in addition to PIN entry. The method involves comparing current biometric data with historical data to ensure consistency. This prevents unauthorized use of stolen cards or compromised PINs. A generative adversarial network (GAN) is trained to enhance biometric data and generate distributions for verification. It analyzes discrepancies and determines if multiple users could match the data.

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Enabling voice biometric authentication for user accounts that allows non-intrusive collection of voice data during normal calls to enroll users and authenticate them without disrupting the call flow. The system enhances audio quality during calls to capture voice samples for enrollment. It filters background noise to focus on high-definition voice characteristics. This minimally intrusive enrollment is performed during normal conversations between users and agents. The enhanced audio is stored and converted to voiceprints for authentication.

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Authenticating user identity in digital services and voice calls using voice analysis instead of passwords or security questions. The method involves comparing a user's recorded voice speaking a unique identifier against their stored voice record to authenticate. Speech recognition extracts the identifier, and voice comparison verifies identity. It enables hands-free login and call access without manual entry or remembering passwords.

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Training a machine learning model for simultaneously authenticating a user and determining liveness of the user's biometric voice data. The training involves using a dataset of audio signals with user identities and flags indicating if the audio was live or synthetic. The model learns embeddings from the audio signals and optimizes losses like triplet loss to successfully verify user identity and liveness. This joint training allows the model to authenticate users and detect spoofing attacks like voice cloning or conversion.

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