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AI assisted fraud detection and prevention system

ORDINARY APPLICATION

Published

date

Filed on 13 November 2024

Abstract

This patent describes a system and method for real-time fraud detection and prevention, leveraging artificial intelligence (AI) and machine learning algorithms to analyze transactional and behavioral data. It integrates various data sources to identify fraudulent activities across domains, such as financial transactions, e-commerce, and identity verification. The system mitigates fraud risks by promptly flagging suspicious transactions and recommending preventive measures. A system and method for detecting and preventing fraudulent activities using artificial intelligence (AI) is disclosed. The system utilizes machine learning algorithms to analyze data from various sources, including transactional data, user behavior data, and external data feeds. The system extracts relevant features from the data, trains machine learning models using labeled datasets, and scores transactions in real-time to identify potentially fraudulent activity. The system also includes a decision engine that uses the risk score to make decisions on whether to approve, reject, or flag a transaction for further review. The system continuously updates and improves the machine learning models using feedback from users, analysts, and other stakeholders. The system provides improved accuracy, real-time detection, and adaptability to new types of fraud.

Patent Information

Application ID202441087504
Invention FieldCOMPUTER SCIENCE
Date of Application13/11/2024
Publication Number47/2024

Inventors

NameAddressCountryNationality
Dr A.C. SANTHA SHEELA, Sathyabama Institute of Science & TechnologyAssociate Professor, Department of Computer Science and Engineering, School of Computing, Sathyabama Institute of Science & Technology, Chennai - 600119, IndiaIndiaIndia
Ms.J.DEEPA, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyAssistant Professor, Department of Information, Technology, School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Chennai, IndiaIndiaIndia
Dr.S.SHALINI, Sathyabama Institute of Science & TechnologyAssistant Professor, Department of Computer Science and Engineering, School of Computing, Sathyabama Institute of Science & Technology, Chennai - 600119, IndiaIndiaIndia
Mrs.G.SANGEETHA, PERI Institute of TechnologyAssistant Professor, Department of Computer Science and Engineering, PERI Institute of Technology, Chennai, IndiaIndiaIndia
Mr.MUTHU V, Panimalar Engineering CollegeAssistant Professor, Department of AI&ML, Panimalar Engineering College, Chennai, IndiaIndiaIndia
Ms.KEERTHANA P, Sathyabama Institute of Science & TechnologyAssistant Professor, Department of Computer Science and Engineering, School of Computing, Sathyabama Institute of Science & Technology, Chennai - 600119, IndiaIndiaIndia
Dr. PRABU M, Amrita Vishwa VidyapeethamAssistant Professor (Sl. Gr.), Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Chennai, 601103IndiaIndia
Dr.G.ANBU SELVI, Sathyabama Institute of Science & TechnologyAssistant Professor, Department of Computer Science and Engineering, School of Computing, Sathyabama Institute of Science & Technology, Chennai - 600119, IndiaIndiaIndia
Dr.N.S.USHA, Sathyabama Institute of Science & TechnologyAssociate Professor, Department of Computer Science and Engineering, School of Computing, Sathyabama Institute of Science & Technology, Chennai- 600119, IndiaIndiaIndia
Dr.E.Murali, Sathyabama Institute of Science & TechnologyAssociate Professor, Department of Computer Science and Engineering, School of Computing, Sathyabama Institute of Science & Technology, Chennai- 600119, IndiaIndiaIndia

Applicants

NameAddressCountryNationality
Dr A.C. SANTHA SHEELA, Sathyabama Institute of Science & TechnologyAssociate Professor, Department of Computer Science and Engineering, School of Computing, Sathyabama Institute of Science & Technology, Chennai - 600119, IndiaIndiaIndia
Ms.J.DEEPA, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyAssistant Professor, Department of Information, Technology, School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Chennai, IndiaIndiaIndia
Dr.S.SHALINI, Sathyabama Institute of Science & TechnologyAssistant Professor, Department of Computer Science and Engineering, School of Computing, Sathyabama Institute of Science & Technology, Chennai - 600119, IndiaIndiaIndia
Mrs.G.SANGEETHA, PERI Institute of TechnologyAssistant Professor, Department of Computer Science and Engineering, PERI Institute of Technology, Chennai, IndiaIndiaIndia
Mr.MUTHU V, Panimalar Engineering CollegeAssistant Professor, Department of AI&ML, Panimalar Engineering College, Chennai, IndiaIndiaIndia
Ms.KEERTHANA P, Sathyabama Institute of Science & TechnologyAssistant Professor, Department of Computer Science and Engineering, School of Computing, Sathyabama Institute of Science & Technology, Chennai - 600119, IndiaIndiaIndia
Dr. PRABU M, Amrita Vishwa VidyapeethamAssistant Professor (Sl. Gr.), Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Chennai, 601103IndiaIndia
Dr.G.ANBU SELVI, Sathyabama Institute of Science & TechnologyAssistant Professor, Department of Computer Science and Engineering, School of Computing, Sathyabama Institute of Science & Technology, Chennai - 600119, IndiaIndiaIndia
Dr.N.S.USHA, Sathyabama Institute of Science & TechnologyAssociate Professor, Department of Computer Science and Engineering, School of Computing, Sathyabama Institute of Science & Technology, Chennai- 600119, IndiaIndiaIndia
Dr.E.Murali, Sathyabama Institute of Science & TechnologyAssociate Professor, Department of Computer Science and Engineering, School of Computing, Sathyabama Institute of Science & Technology, Chennai- 600119, IndiaIndiaIndia

Specification

Description:This patent describes a system and method for real-time fraud detection and prevention, leveraging artificial intelligence (AI) and machine learning algorithms to analyze transactional and behavioral data. It integrates various data sources to identify fraudulent activities across domains, such as financial transactions, e-commerce, and identity verification. The system mitigates fraud risks by promptly flagging suspicious transactions and recommending preventive measures. A system and method for detecting and preventing fraudulent activities using artificial intelligence (AI) is disclosed. The system utilizes machine learning algorithms to analyze data from various sources, including transactional data, user behavior data, and external data feeds. The system extracts relevant features from the data, trains machine learning models using labeled datasets, and scores transactions in real-time to identify potentially fraudulent activity. The system also includes a decision engine that uses the risk score to make decisions on whether to approve, reject, or flag a transaction for further review. The system continuously updates and improves the machine learning models using feedback from users, analysts, and other stakeholders. The system provides improved accuracy, real-time detection, and adaptability to new types of fraud. , C , Claims:1. An artificial intelligence-assisted fraud detection and prevention system, comprising:
a. a data ingestion module for collecting and processing data from various sources;
b. a feature engineering module for extracting relevant features from the ingested data;
c. a machine learning module for analyzing the extracted features and identifying patterns indicative of fraudulent activity;
d. a model training module for training and updating the machine learning models using labeled datasets;
e. a real-time scoring module for scoring transactions in real-time using the trained machine learning models;
f. a decision engine module for making decisions on whether to approve, reject, or flag a transaction for further review; and
g. a feedback loop module for collecting feedback from users, analysts, and other stakeholders to continuously update and improve the machine learning models.
2. The system of claim 1, wherein the machine learning module utilizes supervised and unsupervised learning, deep learning, and natural language processing algorithms.
3. The system of claim 2, wherein the decision engine module uses a risk score generated by the real-time scoring module to make decisions on whether to approve, reject, or flag a transaction for further review.

Documents

NameDate
202441087504-COMPLETE SPECIFICATION [13-11-2024(online)].pdf13/11/2024
202441087504-DECLARATION OF INVENTORSHIP (FORM 5) [13-11-2024(online)].pdf13/11/2024
202441087504-DRAWINGS [13-11-2024(online)].pdf13/11/2024
202441087504-FORM 1 [13-11-2024(online)].pdf13/11/2024
202441087504-FORM-9 [13-11-2024(online)].pdf13/11/2024

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