“ARTIFICIAL INTELLIGENCE-BASED FRAUD DETECTION SYSTEM”

Abstract

The present invention provides an artificial intelligence-based fraud detection system designed to identify and prevent fraudulent activities in real-time across various transactional environments, such as financial services, e-commerce, and insurance. The system comprises a data ingestion module that collects and preprocesses data from multiple sources, including transaction histories, user profiles, and device identifiers. A feature extraction module analyzes this data to identify characteristics associated with potential fraud, while an artificial intelligence model module, using both supervised and unsupervised machine learning algorithms, classifies transactions as either legitimate or suspicious. An anomaly detection module monitors deviations from established behavior, flagging unusual activities for further review. A feedback module continuously updates the machine learning models based on confirmed fraud cases, enhancing detection accuracy over time. Additionally, an alert and reporting module generates real-time notifications for flagged transactions, presenting risk scores and insights for swift decision-making. The system offers an adaptable, scalable, and secure solution for detecting fraud across diverse applications, improving both the accuracy of fraud prevention and the user experience by reducing false positives.

Specification

Description:In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features. An individual feature may not address all of the problems discussed above or might address only some of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein.

The ensuing description provides exemplary embodiments only and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the disclosure as set forth.

Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail to avoid obscuring the embodiments.

Also, it is noted that individual embodiments may be described as a process that is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.

The word "exemplary" and/or "demonstrative" is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as "exemplary" and/or "demonstrative" is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms "includes," "has," "contains," and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term "comprising" as an open transition word without precluding any additional or other elements.

Reference throughout this specification to "one embodiment" or "an embodiment" or "an instance" or "one instance" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.

The present invention is an advanced artificial intelligence-based fraud detection system designed to monitor and analyze transactional data across various domains. This system leverages machine learning algorithms to identify fraudulent activities in real-time by processing large datasets and extracting relevant features that indicate potential fraud. The architecture of the system consists of several integral components, including data ingestion, feature extraction, AI model deployment, anomaly detection, a feedback loop, and an alerting mechanism, all of which work in unison to provide robust fraud detection capabilities.

In the first embodiment, the fraud detection system is implemented within a financial institution's online banking platform to secure electronic transactions. The system begins with the Data Ingestion Module, which collects a diverse array of transactional data, including user account information, transaction history, geolocation data, and device identifiers. This data is preprocessed to standardize formats, ensuring that it can be efficiently analyzed by subsequent components.

The feature extraction module identifies key indicators of fraudulent behavior, such as transaction amounts, frequency, and user patterns. For example, if a user typically makes small transactions and suddenly initiates a large transfer, this anomaly can trigger further investigation. Machine learning models, including supervised learning techniques like decision trees and unsupervised methods such as clustering, are employed in the AI Model Module to classify transactions as legitimate or suspicious based on historical patterns of both normal and fraudulent activities.

An anomaly detection module continuously monitors transactions in real-time, comparing them against established behavioral profiles. If a transaction deviates significantly from expected patterns, it is flagged for review. The Feedback Loop allows the system to learn from past fraud cases, enabling continuous improvement of the detection algorithms.

When a flagged transaction is confirmed as fraudulent or legitimate, the AI models are updated accordingly, refining their accuracy over time. The Alert and Reporting Module generates immediate notifications for any flagged transactions, empowering security personnel to take swift action to mitigate risks.

The second embodiment involves the deployment of the fraud detection system within an e-commerce platform to monitor online transactions and identify fraudulent purchases. The Data Ingestion Module aggregates data from user profiles, transaction records, and behavioral metrics, including browsing history and cart activities. This data is meticulously preprocessed to ensure accuracy and relevance, preparing it for deep analysis.

In the feature extraction module, various characteristics specific to e-commerce are analyzed, such as purchase amounts, frequency of purchases, user interaction patterns, and geolocation. Machine learning models within the AI Model Module utilize these features to create a comprehensive profile for each user, allowing for a nuanced understanding of typical buying behavior. Advanced algorithms, including neural networks, are used to detect patterns that might indicate fraudulent activity, such as rapid changes in purchasing behavior or unusual shipping addresses.

The anomaly detection module actively identifies transactions that significantly deviate from established user profiles. For instance, if a user who usually purchases low-cost items suddenly attempts to buy expensive electronics, the system flags this for potential fraud investigation. Continuous feedback from the Feedback Loop allows the system to adjust its parameters based on the results of manual verifications, enhancing its effectiveness over time. The Alert and Reporting Module provides a dashboard for fraud analysts, summarizing flagged transactions and offering insights into patterns of fraud, enabling them to prioritize investigations.

In third embodiment, the fraud detection system is tailored for an insurance company to evaluate and verify claims for potential fraud. The Data Ingestion Module collects a wide range of claim-related data, including claimant information, claim amounts, incident descriptions, and supporting documents. Both structured data (e.g., claim forms) and unstructured data (e.g., narratives and images) are processed to extract meaningful insights.

The feature extraction module focuses on detecting key patterns that may indicate fraudulent claims. For instance, it analyzes the frequency of claims submitted by specific individuals, evaluates the types of claims made, and employs natural language processing (NLP) techniques to analyze claim narratives for common phrases or inconsistencies that could signal fraud.

The AI Model Module integrates various machine learning techniques, such as logistic regression and clustering algorithms, to categorize claims and assess their legitimacy based on learned behaviors from historical claim data.

The anomaly detection module continuously monitors incoming claims for inconsistencies or unusual patterns, such as multiple claims for the same incident or claims that exceed typical values for similar incidents. The Feedback Loop allows for the refinement of AI models by incorporating results from fraud investigations, thereby improving the detection of new fraud tactics over time. Lastly, the Alert and Reporting Module generates detailed reports on flagged claims, providing adjusters with critical insights and risk scores that assist in their decision-making process, thus enhancing operational efficiency and fraud prevention in insurance claims processing.

While considerable emphasis has been placed herein on the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the invention. These and other changes in the preferred embodiments of the invention will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter to be implemented merely as illustrative of the invention and not as limitation. , Claims:1.A system for implementation An artificial intelligence-based fraud detection system comprising:
a data ingestion module configured to collect and preprocess transactional data from multiple sources, including user profiles, transaction histories, geolocation data, and device identifiers;
a feature extraction module configured to analyze and extract fraud-related features from the transactional data, including transaction frequency, amount, geolocation, and user behavior patterns;
an artificial intelligence model module comprising at least one supervised machine learning model and at least one unsupervised machine learning model, the model module configured to:
classify each transaction as potentially fraudulent or legitimate based on the extracted fraud-related features;
an anomaly detection module configured to detect deviations from established user profiles or transactional norms, comparing each transaction to historical data associated with a user;
a feedback module configured to update the artificial intelligence model module with information from confirmed fraudulent and non-fraudulent transactions, thereby enhancing classification accuracy over time;
an alert and reporting module configured to generate notifications for flagged transactions, wherein each notification includes a confidence score associated with the likelihood of fraud.

2.The fraud detection system of claim 1, wherein the data ingestion module further comprises a data normalization component that standardizes data across multiple formats and sources, ensuring consistent input for the feature extraction module.

3.The fraud detection system of claim 1, wherein the feature extraction module includes a behavioral analysis submodule that tracks and profiles user behavior patterns over time, identifying typical transaction characteristics based on frequency, spending amount, and location.

4.The fraud detection system of claim 1, wherein the alert and reporting module includes a user interface that displays flagged transactions along with detailed risk indicators, such as the fraud likelihood score, anomalous features, and historical comparison data for review by security analysts.

5.The fraud detection system of claim 1, wherein the system is configured to integrate data encryption protocols to protect transactional data throughout the fraud detection process, thereby maintaining the privacy and security of user information.

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