MACHINE LEARNING-ASSISTED FRAUD DETECTION SYSTEM

Abstract

The present invention relates to a machine learning-assisted fraud detection system that efficiently identifies and prevents fraudulent activities in real-time. By leveraging both supervised and unsupervised machine learning algorithms, the system analyzes large volumes of transactional data, detects abnormal patterns, and classifies transactions as fraudulent or legitimate. It incorporates advanced techniques such as anomaly detection, feature extraction, and continuous model training to improve accuracy and adapt to emerging fraud tactics. The system is designed for scalability, real-time processing, and minimal false positives, making it suitable for applications in financial institutions, e-commerce platforms, and mobile payments.

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 discloses a machine learning-assisted fraud detection system designed to detect and prevent fraudulent activities in real-time. The system utilizes advanced machine learning techniques to identify patterns in transactional data, adapting to new fraud scenarios and minimizing both false positives and false negatives. It is capable of processing large volumes of data in real-time, ensuring timely detection of potentially fraudulent activities.
The system includes a data collection module, which gathers transactional data from various sources such as financial institutions, e-commerce platforms, and mobile payment applications. The data can include transactional details such as amounts, times, locations, account information, user behavior patterns, and other relevant features. This data is then passed to the pre-processing module, which cleans and normalizes the data to ensure that it is consistent and suitable for analysis by the machine learning model.

Next, a feature extraction module identifies and extracts key features from the data, which are important for detecting fraud. These features can include transaction amounts, the frequency of transactions, the velocity of funds, account behavior patterns, geographical locations, and more. This step is crucial for preparing the data for training machine learning models, as the right set of features can significantly enhance detection accuracy.

The heart of the system is the machine learning model. The model is trained using historical data, including both labeled (fraudulent and non-fraudulent) and unlabeled data. Supervised learning algorithms, such as decision trees, random forests, or support vector machines (SVM), are applied to classify transactions. Additionally, unsupervised learning techniques, such as clustering or anomaly detection methods (e.g., k-means, DBSCAN), are employed to identify new and unknown fraud patterns. The model continues to learn from new data inputs, thus improving over time.

To identify transactions that deviate from known patterns, the anomaly detection module monitors for outliers or abnormal patterns in real-time. This component uses statistical techniques and clustering algorithms to identify transactions that do not match typical transaction behaviors. These anomalies are flagged as potentially fraudulent for further review by the system.

A real-time fraud detection engine processes the incoming transactions and compares them to the trained machine learning model's predictions. The engine classifies transactions as fraudulent or legitimate. If a transaction is flagged as suspicious, the system generates an alert for investigation or automatically initiates predefined actions, such as blocking or flagging the transaction.

A crucial feature of the system is the feedback loop mechanism, which allows continuous improvement. The feedback loop enables the system to learn from new fraud cases, retrain models with updated data, and improve fraud detection accuracy over time. This adaptive learning ensures that the system evolves alongside emerging fraud techniques, thereby maintaining its relevance and efficacy in a dynamic fraud landscape.

The system is designed to be scalable, capable of handling large volumes of transactional data in real time. Its modular architecture ensures flexibility, allowing easy integration with existing transaction systems in banks, e-commerce platforms, or mobile payment applications. Additionally, the system is optimized to operate with minimal latency, ensuring that fraudulent transactions are detected and acted upon immediately.

In one embodiment, the machine learning-assisted fraud detection system is implemented within a financial institution's payment gateway. The system collects transactional data such as transaction amount, sender and receiver details, transaction history, location, and device information. The data is pre-processed, cleaned, and normalized before feature extraction, where key attributes like transaction frequency and geographical location are identified. The machine learning model is trained using a combination of historical fraud data and legitimate transactions, employing supervised learning techniques like decision trees and random forests.

The fraud detection engine analyzes incoming transactions in real time, classifying them as fraudulent or legitimate based on the model's predictions. Suspicious transactions are flagged and automatically blocked or held for further review. Additionally, the system learns from ongoing transactions, improving its detection capabilities with each new fraud case. The continuous feedback loop ensures that the model is updated regularly to adapt to emerging fraud trends.

In a second embodiment, the system is deployed within an e-commerce platform to monitor and detect fraudulent activities such as account takeovers, payment fraud, and fake reviews. The system collects data from user transactions, including items purchased, payment methods, delivery addresses, and user behavior data such as browsing patterns and login frequencies. After preprocessing, the system uses feature engineering techniques to extract behaviors that are indicative of fraudulent activities, such as rapid repeated transactions from a single IP address or unusual purchase patterns.

Using unsupervised learning techniques, the system can detect novel fraud behaviors that have not been encountered previously. Anomaly detection algorithms flag outlier transactions, such as sudden spikes in purchase amounts or abnormal product returns, as potentially fraudulent. When such transactions are detected, the system can trigger automatic actions, such as blocking the transaction or requesting additional verification from the user. The system continuously adapts to new fraud strategies through its feedback loop, ensuring high detection accuracy over time.

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 method for fraud detection using machine learning, comprising:
Collecting transactional data in real-time;
Pre-processing the collected data for feature extraction;
Applying a machine learning model to classify transactions as fraudulent or non-fraudulent;
Detecting anomalies in transaction patterns using an anomaly detection algorithm;
Generating alerts or automated responses based on the classification.

2.The method of claim 6, wherein the machine learning model is trained using a combination of supervised and unsupervised learning techniques.

3.The method of claim 6, wherein the feedback loop further trains the machine learning model to adapt to new patterns of fraud.

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