An enhanced AI-powered system for multimodal data processing, dynamic authentication, and secure fintech and IoT transactions.

Abstract

An AI-powered system (100) for multimodal data processing, dynamic authentication and secure FinTech and IoT transactions, which includes the following: a multimodal data processing module configured to process structured, semi-structured and unstructured data, including financial transactions, IoT sensor data, voice input and image-based data; an AI-powered dynamic authentication module configured to provide behavioral biometrics, context analysis, and multi-factor authentication (MFA) for continuous user and device verification; an anomaly detection module integrated with machine learning models and configured to provide real-time fraud detection by analyzing transaction patterns, user behavior, and device interactions; a secure encryption module configured to use homomorphic encryption and secure multi-party computations to protect sensitive financial and IoT data; and a blockchain-based transaction validation module configured to create an immutable, tamper-proof ledger to ensure data integrity and transaction transparency.

Description

[0001] The present invention relates to an advanced, artificial intelligence (AI)-driven system for secure and efficient data processing in FinTech and IoT applications. In particular, the invention relates to an AI-driven framework for multimodal data processing, dynamic real-time authentication, and encrypted transaction execution for enhanced cybersecurity.

[0002] The increasing adoption of financial technology (FinTech) and Internet of Things (IoT) solutions has led to a significant rise in digital transactions and machine-to-machine (M2M) communication. These advancements have enabled seamless payment processing, automated device interactions, and real-time data analytics across various industries. However, traditional authentication mechanisms, static encryption methods, and rule-based fraud detection procedures pose significant challenges in terms of security, privacy, and efficiency. Traditional authentication systems based on passwords, PINs, and biometric authentication are vulnerable to phishing, spoofing, and SIM swapping attacks, making unauthorized access and identity theft a major concern.Furthermore, existing data processing architectures struggle to process unstructured and semi-structured data, leading to delays in decision-making and ineffective fraud detection.

[0003] Traditional, rule-based fraud detection mechanisms lack adaptability, resulting in a high number of false positives and negatives, while simultaneously failing to recognize evolving cyber threats. Furthermore, these systems do not incorporate behavioral analytics, such as keystroke dynamics and user interaction patterns, which are crucial for real-time anomaly detection. Additionally, conventional encryption methods like AES and RSA introduce computational inefficiencies and vulnerabilities at specific points, making them susceptible to cyberattacks. In IoT environments, devices and transactions are vulnerable to breaches due to security weaknesses such as static authentication keys, inadequate real-time anomaly detection, and the absence of blockchain-based integrity checks.

[0004] Due to these limitations, there is an urgent need for an AI-powered system capable of dynamically authenticating users and devices by leveraging behavioral biometrics, contextual data, and AI-driven risk assessments. Furthermore, efficient, real-time, multimodal data processing is essential for accurate fraud detection and decision-making. To enhance security, the integration of blockchain and homomorphic encryption can ensure secure transactions without exposing raw data. An adaptive anomaly detection mechanism, continuously refined to address emerging cybersecurity threats, is crucial for protecting FinTech and IoT ecosystems.The present invention addresses these challenges by introducing an AI-enhanced multimodal data processing system with dynamic authentication and secure transaction capabilities, ensuring a high level of security, efficiency, and reliability.

[0005] To solve this problem, the present invention offers an improved AI-supported system for multimodal data processing, dynamic authentication, and secure fintech and IoT transactions.

[0006] The enhanced AI-powered system for multimodal data processing, dynamic authentication, and secure FinTech and IoT transactions can continuously verify users and IoT devices based on behavioral biometrics, contextual data, and multi-factor authentication (MFA), thereby reducing the vulnerabilities associated with static authentication methods.

[0007] The enhanced AI-powered system for multimodal data processing, dynamic authentication, and secure FinTech and IoT transactions can process structured, semi-structured, and unstructured data, including financial data, sensor data, voice commands, and image-based transactions, thus ensuring seamless and efficient decision-making in real-time applications.

[0008] The enhanced AI-powered system for multimodal data processing, dynamic authentication, and secure FinTech and IoT transactions can integrate advanced machine learning models to detect fraudulent activity and unauthorized access attempts by analyzing user behavior, transaction patterns, and device interactions, thereby reducing false positive and negative results in fraud detection.

[0009] The enhanced AI-powered system for multimodal data processing, dynamic authentication, and secure FinTech and IoT transactions can implement advanced cryptographic techniques such as homomorphic encryption and secure multi-party computation to protect sensitive financial and IoT data while ensuring compliance with global security standards and privacy regulations.

[0010] The enhanced AI-powered system for multimodal data processing, dynamic authentication, and secure FinTech and IoT transactions can create an immutable and tamper-proof transaction ledger, ensuring data integrity, transparency, and trust in financial transactions and IoT device communication.

[0011] The enhanced AI-powered system for multimodal data processing, dynamic authentication, and secure FinTech and IoT transactions can develop a self-learning security framework that dynamically adapts authentication and transaction verification processes based on real-time risk assessments, changing user behavior, and evolving cyber threats.

[0012] The enhanced AI-powered system for multimodal data processing, dynamic authentication, and secure FinTech and IoT transactions can minimize the computational effort associated with traditional encryption and authentication methods, thus ensuring real-time transaction processing and seamless integration into existing FinTech and IoT infrastructures.

[0013] The enhanced AI-powered system for multimodal data processing, dynamic authentication, and secure FinTech and IoT transactions can implement secure device-to-device authentication and encrypted communication channels to prevent unauthorized access, data manipulation, and cyberattacks on IoT ecosystems.

[0014] The improved AI-powered system for multimodal data processing, dynamic authentication, and secure FinTech and IoT transactions can provide a smooth authentication and transaction verification process by integrating AI-powered authentication techniques such as voice recognition, facial analysis, and behavior pattern recognition, thereby increasing both security and convenience.

[0015] The enhanced AI-powered system for multimodal data processing, dynamic authentication, and secure FinTech and IoT transactions can be integrated into various FinTech platforms, IoT frameworks, and cloud environments, ensuring seamless deployment and scalability across different industries.

[0016] In one embodiment, an enhanced AI-powered system is provided for multimodal data processing, dynamic authentication, and secure FinTech and IoT transactions. The invention integrates artificial intelligence (AI), machine learning (ML), blockchain, and advanced cryptographic techniques to improve security, efficiency, and reliability in financial and IoT ecosystems. One of the key aspects of the invention is its dynamic authentication mechanism, which continuously verifies users and devices based on biometric behavioral data, contextual data, and adaptive risk assessments.Unlike traditional static authentication methods such as passwords or OTPs, this system leverages AI-driven techniques, including keystroke dynamics, voice recognition, and facial analysis, to enable real-time identity verification while minimizing vulnerabilities such as phishing and credential theft. The system also features an advanced multimodal data processing engine that seamlessly handles structured, semi-structured, and unstructured data, including financial transactions, sensor data, voice commands, and image-based input. This ensures efficient, real-time decision-making, particularly in fraud detection and risk assessment. To guarantee end-to-end data security, the system utilizes homomorphic encryption, secure multi-party computation, and blockchain technology for secure transaction processing without exposing raw data.Blockchain integration guarantees an immutable and tamper-proof ledger, increasing transparency and trust in financial and IoT transactions. Furthermore, the system features an AI-powered anomaly detection system that continuously monitors transaction patterns and device interactions, identifying fraudulent activity in real time. The system also optimizes computing efficiency and reduces latency in authentication and transaction verification processes. By combining AI-driven authentication, adaptive fraud detection, and secure data encryption techniques, this invention offers a highly secure, scalable, and efficient solution for protecting FinTech and IoT transactions from evolving cyber threats. The invention is explained again below with reference to the figure. This shows: Fig. : an enhanced AI-powered system for multimodal data processing, dynamic authentication and secure FinTech and IoT transactions.

[0017] Fig.This document showcases an enhanced AI-powered system (100) for multimodal data processing, dynamic authentication, and secure FinTech and IoT transactions. The system (100) is designed to improve the security, efficiency, and reliability of financial transaction processing and IoT communication, while overcoming the limitations of traditional authentication and security mechanisms. The system (100) comprises a multimodal data processing module, an AI-driven dynamic authentication module, an anomaly detection module, a secure encryption module, and a blockchain-based transaction validation module, all working in an integrated manner to provide real-time security, authentication, and transaction verification.The multimodal data processing module is configured to process various data types, including structured, semi-structured, and unstructured data derived from financial data, IoT sensor outputs, voice commands, and image-based transactions. This module leverages artificial intelligence (AI) algorithms to classify, analyze, and extract relevant information, enabling secure and efficient transaction processing. The AI-powered dynamic authentication module continuously verifies users and IoT devices using behavioral biometrics, contextual data, and multi-factor authentication (MFA). It includes a biometric analysis module that authenticates users based on keystroke dynamics, facial recognition, speech patterns, and fingerprint analysis.Furthermore, the context-aware authentication module dynamically adjusts security parameters based on risk assessments, user behavior, and device interactions, ensuring adaptive security. The anomaly detection module integrates machine learning (ML) and deep learning algorithms to detect fraudulent activity and unauthorized access attempts. It monitors transaction patterns, user behavior, and device communication to identify anomalies in real time, thereby reducing false positives and false negatives in fraud detection. The secure encryption module ensures the confidentiality and integrity of financial and IoT data using homomorphic encryption, secure multi-party computation, and post-quantum cryptography techniques. This module enables secure data transmission and processing without exposing raw data to potential threats.Furthermore, it enables confidential calculations and ensures that sensitive data remains protected even during these calculations. The blockchain-based transaction validation module enhances the security, transparency, and immutability of transactions through integration with decentralized ledger technology (DLT). It provides a tamper-proof, verifiable record of all transactions, thus guaranteeing auditability and trustworthiness within the FinTech and IoT ecosystem. This module also supports interoperability with multiple frameworks, enabling seamless integration into existing financial and IoT infrastructures. The modules within the system (100) are designed to be interconnected to ensure seamless authentication, security, and data processing.The multimodal data processing module receives and processes transaction-related data from financial systems, IoT devices, and multimedia sources. The processed data is then sent to the AI-driven dynamic authentication module, which verifies the legitimacy of users and devices before authorizing transactions. Simultaneously, the anomaly detection module continuously monitors authentication attempts and transactions, identifying potentially fraudulent activity. If anomalies are detected, the secure encryption module further protects the data by encrypting sensitive transaction details and preventing unauthorized access. The blockchain-based transaction validation module records validated transactions in a distributed ledger, ensuring an immutable audit trail.By integrating these modules, the invention offers a highly secure, intelligent and scalable solution for FinTech and IoT security, ensuring real-time fraud prevention, adaptive authentication and encrypted data processing. Reference symbol list 100 System

Claims

[1] An AI-powered system (100) for multimodal data processing, dynamic authentication and secure FinTech and IoT transactions, comprising: a multimodal data processing module configured to process structured, semi-structured and unstructured data, including financial transactions, IoT sensor data, voice input and image-based data; an AI-driven dynamic authentication module configured to provide behavioral biometrics, contextual analysis, and multi-factor authentication (MFA) for continuous user and device verification; an anomaly detection module integrated with machine learning models and configured to provide real-time fraud detection by analyzing transaction patterns, user behavior, and device interactions; a secure encryption module configured to use homomorphic encryption and secure multi-party computation to protect sensitive financial and IoT data; and a blockchain-based transaction validation module configured to create an immutable, tamper-proof ledger to ensure data integrity and transaction transparency. [2] The system (100) of claim 1, wherein the dynamic authentication module further comprises: a biometric analysis module configured to authenticate users using keystroke dynamics, facial recognition, voice patterns, and fingerprint analysis; a context-aware authentication module that adapts security measures based on real-time risk assessments, device location, and user behavior patterns; and an AI-assisted decision-making module that continuously learns and adapts authentication protocols based on detected security threats. [3] The system (100) of claim 1, wherein the multimodal data processing module is configured to process and analyze financial transactions, IoT device telemetry, and multimedia data using artificial intelligence for improved security and efficiency. [4] The system (100) of claim 1, wherein the anomaly detection module uses deep learning models and pattern recognition to detect fraudulent activities and unauthorized access attempts in real time. [5] The system (100) of claim 1, wherein the secure encryption module implements post-quantum cryptography techniques to increase resilience against future cyber threats. [6] The system (100) of claim 1, wherein the blockchain-based transaction validation module ensures end-to-end traceability and auditability of financial and IoT transactions through decentralized ledger technology. [7] The system (100) of claim 1, wherein the AI-driven dynamic authentication module enables real-time, risk-based authentication adjustments based on user behavior patterns, device context, and transaction sensitivity. [8] The system (100) of claim 1, wherein the multimodal computing module is configured to integrate with cloud-based and edge computing architectures to increase processing speed and security. [9] The system (100) of claim 1, wherein the secure encryption module is configured to enable confidential data processing without exposing raw data to unauthorized parties. [10] The system (100) of claim 1, wherein the blockchain-based transaction validation module is interoperable with multiple FinTech and IoT frameworks, ensuring seamless integration and scalability.

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