AI-based system for the detection and prevention of financial fraud

Abstract

An AI-based system for the detection and prevention of financial fraud (100), consisting of: a data collection and integration module configured to collect, standardize, and store transaction and user data from multiple financial sources; a feature extraction and preprocessing module designed to clean, normalize, and extract relevant behavioral and transactional features from the collected data; a module for behavioral profiling and user pattern analysis, configured to create dynamic user behavior profiles based on historical and real-time data; a machine learning and deep learning module adapted to analyze the aforementioned data using prediction and anomaly detection algorithms to identify potential fraudulent activities; a module for real-time anomaly detection and risk assessment, configured to assign risk ratings to transactions based on context and behavioral parameters; a rules module and a policy management module, adapted to apply institution-specific policies for fraud detection and compliance audits; an alarm generation and case management module configured to generate prioritized alarms for suspected fraudulent activity and organize them into actionable cases; a module for continuous learning and model optimization that is adapted to retrain and update detection algorithms based on feedback from verified fraud cases; and a module for visualization, reporting and audit trails, configured to provide graphical insights, compliance reports and transparent audit logs; all of the aforementioned modules are operationally linked and work together to detect, prevent, and continuously adapt to evolving financial fraud patterns in real time.

Description

[0001] The present invention relates to the field of artificial intelligence and financial technology, in particular systems and methods for detecting and preventing fraudulent activities in financial transactions. It utilizes machine learning, behavioral analysis, and real-time anomaly detection to identify suspicious patterns. The invention aims to improve financial security, reduce fraud losses, and ensure trustworthy digital transactions across banking and payment platforms.

[0002] In recent years, the rapid growth of digital banking, online payments, and financial transactions has significantly increased the risk of fraudulent activity. Traditional rule-based fraud detection systems often fail to keep pace with evolving fraud methods, resulting in delayed detection and substantial financial losses. Fraudsters continuously exploit system vulnerabilities and employ sophisticated techniques such as identity theft, synthetic accounts, and transaction spoofing, rendering manual monitoring and static detection models ineffective.

[0003] To meet these challenges, financial institutions need a smarter and more adaptive solution capable of learning from large and dynamic data streams. Existing systems often lack the ability to analyze complex behavioral patterns, identify hidden correlations, and provide real-time insights. As a result, legitimate transactions are sometimes flagged incorrectly, while actual fraudulent transactions go undetected, undermining both customer trust and operational efficiency.

[0004] The present invention aims to overcome these limitations through an AI-driven approach that combines machine learning, predictive analytics, and anomaly detection techniques. By continuously learning from transaction data and user behavior, the system can proactively identify and prevent emerging fraud patterns with high accuracy. This adaptive model not only minimizes false alarms and financial losses but also improves the efficiency of fraud prevention, thereby ensuring a safe and reliable financial ecosystem.

[0005] One objective of this disclosure is to provide an AI-powered system for detecting financial fraud, capable of monitoring and identifying suspicious transactions in real time. It ensures faster response times and minimizes financial losses by automatically detecting and preventing fraudulent activity.

[0006] Another objective of this disclosure is to enable adaptive learning through machine learning and behavioral analysis. The system continuously updates its models based on new fraud patterns, thus ensuring resilience against evolving threats.

[0007] Another objective of this disclosure is to integrate multiple data sources into a unified platform for comprehensive fraud analysis. By combining data from banking systems, payment gateways, and customer behavior logs, the system gains a comprehensive situational awareness.

[0008] Another objective of this disclosure is to automate the generation of fraud alerts and case management through intelligent prioritization. The system categorizes and evaluates alerts based on risk levels, allowing investigators to initially focus on high-severity cases.

[0009] Another objective of this disclosure is to ensure compliance with regulatory frameworks such as AML, KYC, and GDPR. The system integrates automated policy checks and maintains transparent audit trails for every decision made.

[0010] Another objective of this disclosure is to provide a scalable architecture capable of processing large volumes of financial data in real time. It utilizes a cloud-based infrastructure and distributed computing for uninterrupted performance.

[0011] Another objective of this disclosure is to strengthen user trust and satisfaction by minimizing false positives in fraud detection. Advanced behavioral profiling accurately distinguishes legitimate transactions from fraudulent ones.

[0012] Another objective of this disclosure is to provide intuitive visualization and reporting dashboards for analysts and administrators. The system offers real-time insights into fraud trends, system performance, and financial risk levels.

[0013] The present invention relates to an AI-based system for the detection and prevention of financial fraud, which serves to identify, analyze, and prevent fraudulent financial activities in real time using artificial intelligence, machine learning, and behavioral analysis. It integrates multiple data streams from banking, payment, and transaction systems to create a unified and intelligent framework for fraud detection.

[0014] Another embodiment of the present invention is the data acquisition module, which collects, standardizes, and secures data from various financial sources, thus ensuring reliable input for fraud analysis. It supports real-time and batch processing and guarantees data integrity and confidentiality throughout the entire process.

[0015] Another embodiment of the present invention is the feature extraction and preprocessing module, which refines raw data into structured, high-quality datasets by removing noise, detecting outliers, and extracting behavioral features for model training. This improves the precision and performance of fraud detection algorithms.

[0016] Another embodiment of the present invention is the machine learning and deep learning module, which employs a hybrid approach using supervised and unsupervised models to identify anomalies and fraudulent patterns. It continuously learns from new data, thus ensuring adaptability to new fraud methods.

[0017] Another embodiment of the present invention is the module for behavioral profiling and user pattern analysis, which creates unique behavioral profiles for each user and detects deviations that could indicate suspicious activity. This personalized detection approach reduces false alarms and increases the system's accuracy.

[0018] Another embodiment of the present invention comprises the real-time anomaly detection and risk assessment module, which assigns a risk rating to each transaction based on contextual and behavioral indicators. Transactions exceeding the risk thresholds are automatically flagged, blocked, or forwarded for review by an employee.

[0019] Another embodiment of the present invention is the alarm generation and case management module, which automatically creates alarms, groups related events, and provides investigators with detailed visual reports and a severity-based prioritization. This streamlines fraud investigation and improves response efficiency.

[0020] Another embodiment of the present invention is the visualization, reporting, and audit trail module, which provides interactive dashboards, compliance tracking, and detailed audit logs for greater transparency. Together, these modules work seamlessly to offer an intelligent, adaptive, and scalable solution for the real-time detection and prevention of financial fraud in digital financial ecosystems.

[0021] The present invention relates to an AI-based system for the detection and prevention of financial fraud (100) that utilizes artificial intelligence, machine learning, and behavioral analytics to detect, predict, and prevent fraudulent financial activities in real time. It comprises several interconnected modules, including data acquisition, feature extraction, behavioral profiling, machine learning-based detection, and real-time anomaly assessment, which work together to identify suspicious transactions. The system also includes modules for rule management, alert generation, and continuous learning to ensure adaptability and compliance with financial regulations. A visualization and reporting module provides insights into fraud trends and ensures transparency through audit trails.Together, these modules form an intelligent, scalable and self-evolving framework for protecting digital financial ecosystems. Module for data acquisition and integration

[0022] This module is responsible for capturing and integrating data from various financial sources, including banking systems, payment gateways, e-wallets, credit card networks, and customer databases. It ensures a seamless data flow via secure APIs and ETL pipelines while standardizing formats for structured and unstructured data. The module supports both real-time and batch data capture, enabling continuous transaction monitoring. It also performs data cleansing, normalization, and anonymization to ensure accuracy and compliance with data protection regulations. Feature extraction and data preprocessing module

[0023] After data collection, this module extracts meaningful features that can represent transaction and behavioral characteristics. It analyzes attributes such as transaction frequency, amount, device ID, geolocation, and user interaction history. Using statistical and deep learning-based preprocessing techniques, it eliminates noise, fills in missing values, and scales data for optimal model performance. The module ensures that high-quality, feature-rich datasets are available for downstream fraud detection models. Module for behavioral profiling and analysis of user patterns

[0024] This module creates individual behavioral profiles for customers by learning from historical transaction records, spending habits, and device usage patterns. It continuously updates user profiles using unsupervised and reinforcement learning algorithms to capture evolving behaviors. Any significant deviation from the defined profile—such as unusual location, time, or transaction type—is flagged for further analysis. This enables personalized fraud detection and minimizes false positives caused by atypical but legitimate behavior. Module for fraud detection based on machine learning

[0025] The core of the invention is this module, which employs supervised and unsupervised machine learning algorithms to detect anomalies and fraudulent patterns. Techniques such as Random Forest, Gradient Boosting, autoencoders, and graph neural networks are used to identify subtle correlations between transaction parameters. The models are continuously trained and retrained with new data to adapt to emerging fraud methods. This predictive approach improves accuracy and early detection capabilities. Deep Learning and neural network module

[0026] This module augments traditional machine learning models with deep neural networks capable of detecting complex, high-dimensional fraud signatures. Utilizing architectures such as LSTM and CNN, it analyzes sequential transaction data and temporal dependencies to identify hidden indicators of fraud. The deep learning model also supports multimodal input, combining text, image, and metadata features, for example, in document verification or identity checks. It improves real-time fraud detection by learning from massive datasets. Module for real-time anomaly detection and risk assessment

[0027] This module monitors live transaction streams and performs real-time anomaly detection using streaming analytics and event-driven AI. It assigns each transaction a dynamic risk score based on several parameters, such as user trust level, transaction history, and contextual behavior. High-risk transactions are flagged for review or automatically blocked, depending on predefined confidence thresholds. The adaptive scoring engine ensures fast and accurate decision-making to prevent fraudulent activity immediately. Rule engine and policy management module

[0028] This module allows financial administrators to define, update, and manage custom fraud detection rules and compliance policies. It provides a flexible framework where AI-generated insights and domain expertise can coexist. The rule engine dynamically applies these policies, enabling hybrid detection through heuristic and AI-driven mechanisms. It ensures the system complies with financial regulations such as KYC, AML, and GDPR while maintaining operational adaptability. Module for alarm generation and case management

[0029] When suspicious activity is detected, this module generates intelligent alerts that are prioritized based on severity and confidence level. It groups related alerts into unified cases for investigators, reducing redundancy and improving workflow efficiency. The module integrates with dashboards and communication tools to provide detailed visualizations of fraudulent activity, enabling analysts to quickly review, escalate, or dismiss cases. Automated triage and feedback loops further enhance response speed and accuracy. Module for continuous learning and model optimization

[0030] This module ensures the system remains effective against evolving fraud patterns by employing continuous learning strategies. Feedback from confirmed fraud cases and analyst input is used to retrain and optimize models. It utilizes reinforcement learning and model drift detection techniques to guarantee optimal performance over the long term. The module also performs automated feature selection and hyperparameter optimization to ensure model predictions remain accurate and up-to-date. Module for visualization, reporting and audit trail

[0031] This final module provides an intuitive visualization dashboard for decision-makers and auditors to track system performance, risk levels, and fraud trends. It generates comprehensive reports summarizing detection accuracy, false positive rates, and financial impact. The audit trail component logs every system action, decision, and model update to ensure transparency and regulatory compliance. This guarantees accountability, facilitates external audits, and strengthens stakeholder confidence in the AI-powered fraud detection framework.

[0032] The invention will be explained again below with reference to the figures. These show: Fig. : illustrates the structural process and functional networking of different modules within the AI-based system for the detection and prevention of financial fraud (100).

[0033] Illustration of the structural flow and functional connection of various modules within the AI-based system for the detection and prevention of financial fraud (100). The operation of the AI-based system for the detection and prevention of financial fraud (100) begins with the continuous collection of data from various financial sources, such as bank servers, transaction databases, and payment gateways, via the data collection and integration module, which standardizes and secures incoming data streams. The feature extraction and preprocessing module then refines this data by cleaning, normalizing, and deriving meaningful behavioral and transactional features. The behavioral profiling module dynamically creates user profiles based on historical activity and updates them in real time to reflect legitimate output patterns.These enriched data inputs are processed by the machine learning and deep learning modules, which jointly analyze transactions using advanced algorithms and neural networks to detect deviations from normal behavior and identify potential fraudulent activity.

[0034] Simultaneously, the real-time anomaly detection module assigns risk ratings to ongoing transactions and flags high-risk transactions for immediate review or automatic blocking. The Rule Engine and Policy Management module enforces institution-specific regulations and integrates expertise to ensure compliance with AML, KYC, and data protection standards. When anomalies are detected, the Alert Generation and Case Management module generates intelligent alerts, groups related incidents into cases, and provides investigators with detailed insights via an interactive dashboard. Meanwhile, the Continuous Learning module refines detection models based on analyst feedback and confirmed fraud cases, enabling the system to evolve and remain resilient against new fraud methods.Finally, the visualization, reporting, and audit trail module provides transparent reports, detailed analyses, and a secure log of all system actions for compliance and audit purposes. Together, these interconnected modules work seamlessly to enable proactive, real-time fraud detection and prevention, as well as continuous self-improvement across the entire financial ecosystem.

Claims

[1] An AI-based system for the detection and prevention of financial fraud (100), consisting of: a data collection and integration module configured to collect, standardize, and store transaction and user data from multiple financial sources; a feature extraction and preprocessing module designed to clean, normalize, and extract relevant behavioral and transactional features from the collected data; a module for behavioral profiling and user pattern analysis, configured to create dynamic user behavior profiles based on historical and real-time data; a machine learning and deep learning module adapted to analyze the aforementioned data using prediction and anomaly detection algorithms to identify potential fraudulent activities; a module for real-time anomaly detection and risk assessment, configured to assign risk ratings to transactions based on context and behavioral parameters; a rules module and a policy management module, adapted to apply institution-specific policies for fraud detection and compliance audits; an alarm generation and case management module configured to generate prioritized alarms for suspected fraudulent activity and organize them into actionable cases; a module for continuous learning and model optimization that is adapted to retrain and update detection algorithms based on feedback from verified fraud cases; and a module for visualization, reporting and audit trails, configured to provide graphical insights, compliance reports and transparent audit logs; all of the aforementioned modules are operationally linked and work together to detect, prevent, and continuously adapt to evolving financial fraud patterns in real time. [2] System (100) according to claim 1, wherein the data acquisition module and integration module uses secure APIs and ETL pipelines to collect real-time data from bank servers, payment gateways and transaction databases. [3] System (100) according to claim 1, wherein the feature extraction and preprocessing module uses statistical and AI-based techniques to remove noise, identify outliers and increase feature relevance for improved model accuracy. [4] System (100) according to claim 1, wherein the behavior profiling and user pattern analysis module continuously updates user profiles using unsupervised learning to capture changing financial behavior. [5] System (100) according to claim 1, wherein the machine learning and deep learning module implements supervised, unsupervised and neural network models including Random Forest, Autoencoders and LSTM networks for fraud pattern detection. [6] System (100) according to claim 1, wherein the real-time anomaly detection and risk assessment module assigns dynamic risk values ​​to transactions based on device ID, geolocation, transaction speed and user trust index. [7] System (100) according to claim 1, wherein the rule module and the policy management module ensure compliance with financial regulations including Anti-Money Laundering (AML), Know Your Customer (KYC) and General Data Protection Regulation (GDPR). [8] System (100) according to claim 1, wherein the alarm generation and case management module automatically categorizes alarms according to severity and provides investigators with visual dashboards and historical case correlations. [9] System (100) according to claim 1, wherein the continuous learning and model optimization module uses enhanced learning to adapt models based on verified fraud results and feedback from human analysts. [10] System (100) according to claim 1, wherein the module for visualization, reporting and audit trail generates real-time analyses, summaries of fraud trends and secure audit trails for internal and regulatory reviews.

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