AI assisted fraud detection and prevention system

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.

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.

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