In-streaming application for financial services for LLM-based operating systems

1 . A fraud prevention and analysis system for financial services, comprising:

a computing device comprising at least one processor, a memory, a camera, a microphone, a GPS sensor, a gyroscope, and a near-field communication (NFC) reader, wherein the memory stores instructions that, when executed by the at least one processor, configure the computing device to provide:

a. a Large Language Model (LLM) based operating system configured to continuously analyze multimodal data from a plurality of independent channels, said multimodal data comprising:

i. vision data from visual sensors or cameras for capturing images or videos;

ii. auditory data from audio sensors or microphones for processing various audio outputs;

iii. tactile data from tactile sensors or devices capable of detecting vibrations or movement;

iv. gustatory data incorporating taste;

v. olfactory data incorporating smell;

vi. thermoceptive data sensing heat;

vii. nociceptive data recognizing pain; and

viii. equilibrioceptive data maintaining balance;

b. a plurality of fraud prevention modules integrated into the LLM-based operating system, wherein said modules include:

i. a multimodal biometric authentication module utilizing visual, auditory, and tactile data to authenticate an account holder initiating a financial transaction, including verifying login credentials;

ii. a transaction anomaly detection module analyzing transaction patterns using visual, auditory, and tactile data to generate risk assessment data for dispute resolution and fraud detection based on at least transaction data and phone sensor data;

iii. a geospatial analysis module configured to: (1) store a first GPS location in metadata of at least one image of a payment card or check captured by the camera; (2) transmit a link via SMS or email to a cardholder and receive a virtual signature; (3) store signature meta data including a second GPS location at a time of signature capture; and (4) determine a card status comprising a card-present status when the first GPS location and the second GPS location are within a predetermined distance;

iv. a behavioral analysis module utilizing visual, auditory, and tactile data to assess user behavior including, during an NFC reading session, using at least one of the gyroscope to detect micro movements for tap presence and the microphone to record sounds and detect distance from a card to the computing device as proof of transaction; and

v. a third-party data integration module fetching external data sources comprising at least one of a credit score, social media profile, IP address history, user-agent history, history of phone calls, connected funding sources, movement of funds between funding sources, email domain, original registration information, or proof of digital identity from blockchain services, for inclusion in the risk assessment data; and

c. a feedback mechanism configured to:

(i) pre-validate, via an OCR engine, at least one image captured by the camera to detect blurriness or glare using at least one of (A) computing a Fast Fourier Transform (FFT) and examining a distribution of low and high frequencies and (B) a thresholding glare detection method, and prompt a user to retake the at least one image when blurriness or glare is detected; and

(ii) when the card status is the card-present status, convert the card-present status into card-not-present status by executing a non-standard NFC reading algorithm comprising connecting, powering and reading an NFC chip on the card using the NFC reader, querying the NFC chip for its data files, extracting at least one of a primary account number (PAN) and an expiration date from the data files by decoding NFC records encoded in tag-length-value (TLV) format including tag 0x5A, performing a zero-dollar transaction to verify card validity and to store a token, extracting at least one of computing device ID number and phone number, and sending the token and extracted data to a processor for transaction risk reduction and fraud prevention.

2 . The fraud prevention and analysis system of claim 1 , wherein the LLM-based operating system dynamically adjusts fraud prevention strategies based on real-time analysis of multimodal data and external information from third-party data sources.

3 . The fraud prevention and analysis system of claim 1 , further comprising a machine learning component within the LLM-based operating system, the machine learning component continuously evolving based on detected fraud patterns and user interactions.

4 . The fraud prevention and analysis system of claim 1 , wherein the LLM-based operating system generates real-time alerts or notifications through any of the modalities to inform users or financial institutions of potential fraudulent activities.

5 . A method for preventing and analyzing fraud in financial services, comprising the steps of:

a. continuously collecting multimodal data from a plurality of independent channels, including visual, auditory, and tactile data including capturing, by a camera of a computing device, at least one image of a payment card or check and storing a first GPS location in metadata of the at least one image;

b. analyzing the multimodal data using a Large Language Model (LLM) based operating system and fraud prevention modules to detect and prevent fraudulent activities including:

(i) pre-validating, via an OCR engine, the at least one image to detect blurriness or glare using at least one of (A) computing a Fast Fourier Transform (FFT) and examining a distribution of low and high frequencies and (B) a thresholding glare detection method, and prompting a user to retake the at least one image when blurriness or glare is detected;

(ii) transmitting a link via SMS or email to a cardholder and receiving a virtual signature, and storing signature meta data including a second GPS location at a time of signature capture;

(iii) determining a card status comprising a card-present status when the first GPS location and the second GPS location are within a predetermined distance; and

(iv) when the card status is the card-present status, converting the card-present status into card-not-present status by executing a non-standard NFC reading algorithm comprising connecting, powering and reading an NFC chip on the card, querying the NFC chip for its data files, extracting at least one of a primary account number (PAN) and an expiration date from the data files by decoding NFC records encoded in tag-length-value (TLV) format including tag 0x5A, and performing a zero-dollar transaction to verify card validity and to store a token, extracting at least one of computing device ID number and phone number, and sending the token and extracted data to a processor for transaction risk reduction and fraud prevention;

c. integrating third-party data from external sources comprising at least one of a credit score, social media profile, IP address history, user-agent history, history of phone calls, connected funding sources, movement of funds between funding sources, email domain, original registration information, or proof of digital identity from blockchain services, and generating risk assessment data used for dispute resolution and fraud detection; and

d. communicating with users through any of the modalities including at least one of transmitting the prompt to retake the at least one image and transmitting the link via SMS or email for remote virtual signature capture.