Adaptive multi-stage AI system for real-time e-commerce fraud detection

Abstract

Adaptive multi-stage AI system (100) for real-time e-commerce fraud detection, consisting of: a) a data ingestion and pre-processing module for collecting, cleaning and preparing transaction data from various sources for further analysis; b) a multi-level fraud detection module that performs fraud detection at three levels: session level, transaction level and user level; c) an adaptive learning and model development module that continuously retrains fraud detection models using new data and feedback to adapt to new fraud tactics; (d) a risk assessment and fraud prevention module that assigns dynamic risk scores to transactions and applies fraud prevention measures based on predefined thresholds; (e) an integration and user interface module that provides seamless integration with e-commerce platforms and enables real-time monitoring and management through an intuitive user interface; f) a compliance and reporting module that ensures compliance with legal standards such as GDPR and PCI-DSS and generates audit reports for transparency and legal compliance.

Description

[0001] The present invention relates to the field of artificial intelligence, machine learning and fraud detection within e-commerce platforms and focuses in particular on real-time detection of multi-level fraud activities using adaptive algorithms.

[0002] The rapid growth of e-commerce has significantly transformed the retail landscape, enabling businesses to reach global markets and consumers to shop anytime, anywhere. However, this growth has also led to an increase in fraudulent activities, ranging from payment fraud to account takeover and identity theft. As the frequency and sophistication of fraud increases, e-commerce platforms must deploy advanced solutions to detect and mitigate these risks in real time.

[0003] Traditional fraud detection systems often rely on rule-based or static models that struggle to keep pace with fraudsters' constantly evolving tactics. These systems are typically reactive and only detect fraud after it has already occurred, leading to financial losses and damaged customer trust. Furthermore, they can produce false positives, which can miss legitimate transactions and inconvenience customers.

[0004] E-commerce fraud is also highly diverse, as fraudsters employ various techniques such as bot attacks, credit card fraud, and the creation of fake accounts. These activities require a multifaceted approach to detection, as different types of fraud require unique strategies. A one-size-fits-all solution is not suitable for addressing the scale and complexity of fraud across all e-commerce platforms.

[0005] In response to these challenges, the Adaptive Multi-Tier AI System was developed to provide a comprehensive, real-time solution. The system combines artificial intelligence (AI), machine learning (ML), and adaptive algorithms to continuously monitor e-commerce transactions and adapt to emerging threats. By using multiple detection layers, it is able to detect fraud at various stages, from user login to payment transactions, increasing accuracy and reducing the likelihood of false negatives or positives.

[0006] The invention aims to solve both the scalability and adaptability problems of conventional systems. As the volume and complexity of e-commerce transactions increase, the system's modular architecture enables easy integration into existing platforms and continuous updating of detection algorithms. This flexibility ensures that the system remains effective even against new fraud techniques and provides e-commerce companies with a proactive solution for protecting their businesses and customer data.

[0007] An objective of the present disclosure is to enable real-time detection that ensures immediate identification and prevention of fraud and minimizes losses.

[0008] Another subject of the present disclosure is adaptive learning, which allows the system to evolve and stay up to date with emerging fraud tactics.

[0009] Another subject of this disclosure is multi-layered detection, which enables comprehensive analysis and reduces the likelihood of undetected fraud.

[0010] Another objective of this disclosure is a scalable architecture that enables the system to process large volumes of transactions across multiple platforms.

[0011] Another objective of this disclosure is seamless integration, ensuring minimal disruption while complementing existing systems with fraud protection.

[0012] Another objective of this disclosure is to have a customizable risk assessment that enables tailored fraud prevention based on transaction profiles and behaviors.

[0013] Another objective of this disclosure is regulatory compliance, which guarantees that the system meets legal standards such as GDPR and PCI-DSS.

[0014] Another objective of this disclosure is to reduce manual intervention and increase operational efficiency through automated fraud prevention measures.

[0015] The present invention relates to an adaptive AI-driven real-time system for detecting and preventing fraud in e-commerce transactions, which enables immediate identification of suspicious activities as they occur.

[0016] Another embodiment of the present invention is that the system employs a multi-level detection engine that analyzes fraud at the session, transaction, and user levels to detect a wide range of fraudulent behaviors with high accuracy.

[0017] Another embodiment of the present invention is that the system dynamically adapts to new fraud techniques through the use of feedback loops and retrains its models to stay ahead of evolving threats and ensure continuous learning.

[0018] Another embodiment of the present invention is that each transaction is assigned a risk score based on factors such as user behavior, payment method, and historical patterns, enabling automatic or manual fraud prevention measures.

[0019] Another embodiment of the present invention is that the system can be easily integrated with existing e-commerce platforms, payment gateways and authentication systems and provides an intuitive interface for administrators to manage and respond to fraud risks.

[0020] In another embodiment of the present invention, the system includes a compliance module that ensures compliance with regulatory requirements such as GDPR and PCI-DSS while generating audit reports for transparency and regulatory requirements.

[0021] Another embodiment of the present invention is that when suspicious transactions are detected, the system sends real-time alerts to administrators and can block high-risk transactions or automatically initiate further investigations.

[0022] Another embodiment of the present invention is that the modular architecture of the system ensures scalability and flexibility and allows easy upgrades or the integration of additional fraud detection features to address new security threats.

[0023] The present invention relates to the adaptive multi-tier AI system for real-time e-commerce fraud detection, which uses advanced artificial intelligence to monitor and detect fraud in e-commerce transactions. The system consists of several key modules: the Data Ingestion and Preprocessing Module, which aggregates and cleanses transaction data; the Multi-Tier Fraud Detection Module, which analyzes fraud at the session, transaction, and user levels; the Adaptive Learning and Model Evolution Module, which enables the system to adapt to new fraud techniques; the Risk Scoring and Fraud Prevention Module, which assigns risk scores to transactions; the Integration and User Interface Module, which ensures smooth integration with e-commerce platforms; and the Compliance and Reporting Module, which ensures compliance with regulatory standards.Together, these modules form a dynamic, scalable and effective fraud prevention solution.

[0024] The invention is explained again below with reference to the figure. It shows: Fig. : an illustration of an adaptive multi-stage AI system (100) for real-time e-commerce fraud detection.

[0025] Fig.shows an Adaptive Multi-Tier AI System (100) for real-time e-commerce fraud detection. The Adaptive Multi-Tier AI System for Real-Time E-Commerce Fraud Detection works by continuously monitoring e-commerce transactions, detecting suspicious activity, and initiating proactive steps to prevent fraud. First, the system uses its Data Ingestion and Preprocessing Module to collect data in real time from various sources such as user accounts, payment transactions, and browsing behavior. This raw data is cleaned, normalized, and converted into a structured format for further analysis. The Multi-Tier Fraud Detection Module is the heart of the operation and applies a multi-tiered approach to fraud detection. It first examines session-level activity and identifies anomalies such as unusual login patterns or device spoofing.Individual transactions are then examined for inconsistencies, such as mismatched billing and shipping addresses, suspicious payment methods, and transaction patterns that deviate from the norm. Additionally, user behavior is monitored over time to detect persistent fraud indicators across multiple transactions. As the system encounters new types of fraud, the adaptive learning and model evolution module dynamically retrains its detection models, leveraging feedback from previous fraud cases to improve detection accuracy and minimize false positives or negatives.

[0026] The risk assessment and fraud prevention module assigns a risk score to each transaction, taking into account various elements such as the transaction value, user profile, payment method, and historical data. This score triggers a real-time alert to administrators or automatically blocks suspicious transactions if the risk value exceeds a certain threshold. The system also offers seamless integration with e-commerce platforms through its integration and user interface module, allowing administrators to monitor, review, and respond to flagged activities via an intuitive dashboard. Administrators can adjust thresholds, view real-time analytics, and take manual action as needed.Finally, the Compliance and Reporting module ensures that all actions performed by the system comply with legal standards by generating audit reports and ensuring data protection through compliance with regulations such as GDPR and PCI-DSS. Essentially, the system operates autonomously to detect fraud in real time, learn from emerging fraud patterns, and respond dynamically, providing an adaptable, scalable, and robust e-commerce fraud prevention solution.

Claims

[1] Adaptive multi-stage AI system (100) for real-time e-commerce fraud detection, consisting of: a) a data ingestion and pre-processing module for collecting, cleaning and preparing transaction data from various sources for further analysis; b) a multi-level fraud detection module that performs fraud detection at three levels: session level, transaction level and user level; c) an adaptive learning and model development module that continuously retrains fraud detection models using new data and feedback to adapt to new fraud tactics; (d) a risk assessment and fraud prevention module that assigns dynamic risk scores to transactions and applies fraud prevention measures based on predefined thresholds; (e) an integration and user interface module that provides seamless integration with e-commerce platforms and enables real-time monitoring and management through an intuitive user interface; f) a compliance and reporting module that ensures compliance with legal standards such as GDPR and PCI-DSS and generates audit reports for transparency and legal compliance. [2] The system (100) of claim 1, wherein the adaptive learning and model development module includes a feedback loop for refining the detection models based on the results of previous fraud detection events. [3] The system (100) of claim 1, wherein the multi-level fraud detection module uses machine learning algorithms to analyze session-, transaction-, and user-level patterns, thereby improving fraud detection accuracy. [4] The system (100) of claim 1, wherein the risk scoring and fraud prevention module uses both AI-based scoring and rule-based filters to assess the risk level of transactions in real time. [5] The system (100) of claim 1, wherein the integration and user interface module provides API endpoints for easy integration with third-party services and a dashboard for administrators to manage alerts and flagged transactions. [6] The system (100) of claim 1, wherein the compliance and reporting module ensures that all fraud detection processes comply with data protection and security regulations and provides detailed reports for audit purposes. [7] The system (100) of claim 1, wherein the system operates autonomously to detect fraud in real time, with the ability to trigger alerts or automatic preventive actions based on preset thresholds.

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