141 results about "Fraudulent transaction" patented technology

A system for monitoring and optimizing the performance of an e-commerce platform, which includes the following: a) a data collection and integration module configured to retrieve real-time data from e-commerce platforms, marketing channels, inventory systems and user interactions using APIs, tracking scripts and server-to-server communication; b) a data preprocessing and filtering module for cleaning and structuring the collected data by removing inconsistencies, normalizing formats and detecting outliers; c) a machine learning and predictive analytics machine that uses deep learning, regression models and clustering algorithms to analyze historical and real-time data to predict sales trends, customer behavior and demand fluctuations; d) a performance monitoring dashboard configured to visualize key performance indicators, sales trends and marketing effectiveness, and to provide custom reports and real-time alerts; e) an automated module for optimizing marketing campaigns, which dynamically adjusts advertising strategies, budget allocations, target audience approach and creative elements based on real-time data insights; f) an automated decision and control system configured to execute AI-driven optimization strategies, including bid adjustments, price changes and inventory reallocations without manual intervention; (g) a fraud detection and compliance monitoring module that uses anomaly detection algorithms to identify fraudulent transactions, fake reviews and advertising fraud while ensuring compliance with legal regulations; the system continuously monitoring, analyzing and optimizing the performance of the e-commerce platform through automated data-driven decision-making.

A method for detecting fraudulent financial transactions in information technology networks involves obtaining a multitude of features associated with a financial transaction conducted over an information technology network by an unknown transaction party. The multitude of features includes clickstream data obtained from the unknown transaction party. The clickstream data is associated with data of the financial transaction being entered by the unknown transaction party. The method further involves obtaining a first fraud indicator using a machine learning classifier operating on the multitude of features, obtaining a second fraud indicator using a rule-based classifier operating on the multitude of features, obtaining a fraud prediction for the financial transaction, using the first fraud indicator and the second fraud indicator, and taking an action, in response to the fraud prediction.

A method includes receiving, by a processor of a transaction processing entity, a transaction attempt. The method includes receiving a risk score from a risk strategy decision model, the risk score being determined from a machine learning model. The method includes in response to receiving the risk score, determining, whether the risk score exceeds a threshold indicating the transaction attempt is potentially fraudulent. In response to determining the risk score exceeds the threshold: the method includes determining, whether to approve or decline the transaction attempt; and determining a reason for approving or declining the transaction attempt based on one or more variables contributing to the risk score. The method includes outputting an indication to approve or decline the transaction attempt in response to determining whether to approve or decline the transaction attempt and the reason for approving or declining the transaction attempt.

A method for scoring authenticity of a non-fungible token (NFT) using multiple, disparate data sets includes: receiving a scoring request for the NFT; determining a marketplace authenticity score for a marketplace where the NFT is available for purchase based on marketplace metrics; determining a visual authenticity score based on a comparison of visual features of the NFT to visual features of trusted NFTs; determining a wallet authenticity score for a blockchain wallet associated with ownership of the NFT based on a transaction history for the blockchain wallet; calculating a confidence score for the NFT based on a combination of the marketplace, visual, and wallet authenticity scores, the confidence score representing a likelihood that the NFT is authentic; and transmitting the calculated confidence score in response to the received scoring request.

Embodiments provide artificial intelligence methods and systems for evaluating vulnerability risks of issuer authorization system. Method performed by a server system includes accessing a set of payment transaction data including subset of fraudulent transaction data. Method includes generating via a machine learning model, set of synthetic transaction data based on the subset of fraudulent transaction data. Method includes accessing set of historical card velocity features and collating the set of synthetic transaction data and the set of historical card velocity features to generate set of enriched synthetic transaction data. Method includes extracting via a classifier, subset of feasible fraudulent transaction data from the set of enriched synthetic transaction data. Method includes generating simulated authorization model based on the set of payment transaction data. Method includes classifying via simulated authorization model, each enriched synthetic transaction from the subset of feasible fraudulent transaction data as one of fraudulent transaction and non-fraudulent transaction.

Cross-channel user authentication is provided, to increase resilience of a computerized system to cyber-attacks and to fraudulent transactions. In a demonstrative scenario, a user utilizes his web browser on his laptop computer to log-in to a web server; which in turn extracts the IP address of the laptop computer. The web server triggers an application server to send a Push Notification to a Mobile App that had been installed on a smartphone of that user who just logged in; and that Push Notification causes that smartphone to respond to the application server with the IP address of the smartphone. If the IP address of the computer is not identical to the IP address of the smartphone, then the servers determine that the log-in attempt on the computer was possibly fraudulent, and fraud mitigation operations are initiated. Optionally, geo-location data, device orientation data, device motion data, or other parameters are used as part of the cross-channel authentication process.

The invention provides a fraudulent transaction identification method and device, equipment, a storage medium and a program product. Relates to the field of artificial intelligence. The method comprises the steps of obtaining current transaction data corresponding to a current transaction; determining a current transaction feature corresponding to the current transaction data; matching target transaction characteristics according to the current transaction data, wherein the target transaction characteristics comprise a target transaction posterior characteristic, a target transaction address characteristic, a target recent transaction real-time characteristic and a target transaction sequence; and inputting the target transaction characteristics into a fraud identification model trained to converge, and identifying whether the current transaction is a fraud transaction by adopting the fraud identification model trained to converge. According to the method, multi-dimensional feature information is added, fraud transaction behaviors can be fully mined, transaction fraud can be efficiently recognized, and the accuracy of transaction fraud recognition is improved.

In particular embodiments, techniques include a computer-implemented method including monitoring a plurality of real-time streams of content and a plurality of transactions in real-time. A machine-learning model trained to detect fraudulent transaction activity is used to determine that one or more of the real-time streams are associated with fraudulent transaction activity based on input corresponding to the monitored real-time streams and plurality of transactions. A request is received from a user device of a first user to initiate a transaction on behalf of the first user and it is determined that a characteristic of the requested transaction is indicative of a fraudulent transaction associated with the fraudulent transaction activity associated with one or more of the real-time streams. In response, processing of the requested transaction is modified.

A system includes one or more processors to generate a node graph; determine a first node of the node graph comprises a fraudulent flag indicating a first entity of the first node facilitated a fraudulent transaction; responsive to the determination, identify a set of nodes of the node graph responsive to each node of the set of nodes having a direct transaction edge connection with the first node within the node graph or an indirect transaction edge connection with the first node via at least one node directly connected with the first node; generate a fraudulent flag in a subset of the set of nodes responsive to determining each node in the subset satisfies a matching policy; and generate a record identifying the first node and each node of the subset of nodes.

The technology described herein relates to systems, methods, and computer storage media, among other things, for determining whether an electronic transmission (e.g., associated with an electronic payment transaction) should be blocked (e.g., based on being a fraudulent transaction). In embodiments, a policy-based reinforcement learning risk decision agent is used to make these determinations for a plurality of stages associated with the electronic payment transaction (e.g., a pre-authorization stage, a post-authorization stage, and a delay-captured stage). The policy-based reinforcement learning risk decision agent can be trained using previous electronic payment transaction data for previous electronic payment transactions. For example, this particular agent can be trained using pre-authorization electronic payment transaction data, post-authorization electronic payment transaction data, and delay-captured electronic payment transaction data for each of the previous electronic payment transactions.

There is provided a computing system for optimizing a plurality of fraud detection strategies used to generate a corresponding set of potentially fraudulent transactions. The system determines an overall fraud value such as an average fraud value for each transaction based on pre-defined factors and identifies a particular strategy having a highest average fraud value for its fraudulent transactions as a highest priority on a ranked list of strategies. The system is configured to remove each transaction from the remaining other strategies if the same as the fraudulent transactions in the identified strategy and calculate an average fraud value for the remaining other strategies. The system then ranks the next highest priority fraud detection strategy having the highest average fraud value while removing its corresponding transactions flagged from other remaining strategies and repeat the ranking until all the strategies have been ranked and apply the ranked list to subsequent transactions.

The invention relates to the technical field of financial risk management, and discloses a risk control method and system for identifying a transition financial business greening risk, and the method comprises the steps: deconstructing an enterprise commitment into an expected economic behavior vector through a transition knowledge graph, capturing an actual economic behavior data flow, and carrying out the logic consistency matching; according to the method, environmental benefit verification is converted into economic behavior logic chain review, so that a financial institution can avoid high-cost environmental auditing to return to business essence judgment, and meanwhile, bogus transactions are effectively identified through business activeness analysis of a transaction counterparty; and the risk identification capability of the transformed financial business is obviously improved.

The invention relates to the field of information security. A method for detecting fraudulent user transactions comprises the steps of: obtaining a sequence of transactions made by a user within a given time frame, each transaction being characterized by a set of attributes; converting each obtained transaction into an attribute vector on the basis of the corresponding set of transaction attributes; processing the obtained attribute vectors with the aid of a coding machine learning model, with a representation of a sequence of attribute vectors being produced in the form of a hidden state matrix; processing the hidden state matrix with the aid of a generative machine learning model (MLM) trained on hidden state matrices of sequences of transaction attribute vectors, with the generative MLM generating from the hidden state matrix interrelated attribute vectors of one or several transactions; generating the last transaction of the user on the basis of the generated attribute vector; comparing the last transaction made by the user and the generated last transaction of the user by calculating an anomaly assessment of the transaction made; signalling a fraudulent transaction if the anomaly assessment value is above a threshold value. The invention provides for more complete and accurate detection of fraudulent transactions.

A system is adapted to automatically identify suspected fraudulent transactions. The system includes a fraud management server configured to perform these operations: receiving unlabeled transactions, each having a number of features, and storing them in a transaction repository; with the features, determining a risk score for each transaction; based on the risk scores, dividing the unlabeled transactions into bins in order of their risk scores; labeling transactions of the first bin legitimate and those of last bin as fraudulent; with the labeled transactions, training a first machine learning model; with the trained first machine learning model, labeling transactions of a second bin and a second-to-last bin as either fraudulent or legitimate; storing the labeled transactions of the first bin, second bin, second-to-last bin, and last-bin in the transaction repository; and with the labeled transactions of the first bin, second bin, second-to-last bin, and last-bin, training a second machine learning model.

The invention relates to the technical field of digital information processing, in particular to a digital economic transaction processing and anomaly detection method based on machine learning. The method comprises the following steps: carrying out digital transaction data acquisition and weighted feature analysis processing on a digital economic transaction platform to generate weighted digital transaction feature data; analyzing digital transaction strategy prediction data based on the weighted digital transaction feature data; performing abnormal transaction confrontation game feature analysis according to the digital transaction strategy prediction data to generate abnormal transaction confrontation game feature data; establishing a digital transaction fraud tracing tree model based on the abnormal transaction confrontation game feature data; and performing digital transaction abnormal feedback data analysis processing on the weighted digital transaction feature data based on the digital transaction fraud tracing tree model to generate digital transaction abnormal feedback data. According to the invention, by predicting variable and complex fraudulent transaction behaviors in digital transaction behaviors, efficient digital economic transaction anomaly detection is realized.

The invention discloses a commercial bank blacklist management method based on a big data platform and Elasticsearch, and the method comprises the steps: building a blacklist data center based on the big data platform, integrating multi-source heterogeneous data, and carrying out the real-time data cleaning and standardization processing; establishing a distributed real-time retrieval engine by utilizing Elasticsearch, and constructing a reverse index for the processed data; establishing an associated risk map model, dynamically analyzing the association degree between the client and the blacklist main body through a map calculation engine, generating a risk conduction coefficient, and storing the risk conduction coefficient; during business handling, a multi-condition combination query request is initiated to Elasticsearch through an ESB (Enterprise Service Bus) real-time calling interface; a dynamic updating mechanism is adopted, and the blacklist state is automatically updated when a risk threshold value is triggered. According to the method, by integrating multi-source heterogeneous data, millisecond risk interception is realized, a dynamic association graph is constructed, the active defense capability of a bank on risks such as fraudulent transactions, credit default and money laundering behaviors is improved, and the method is suitable for risk management and control of core business scenes such as pre-loan auditing, transaction monitoring and anti-money laundering.

The invention discloses a fraudulent transaction risk monitoring method and device based on a graph neural network, equipment and a medium, and relates to the field of anti-fraudulent, and the method comprises the steps: determining each transaction node and each neighbor entity in a dynamic transaction graph, linking each transaction node with each neighbor entity, and constructing a target static graph; creating new transaction nodes and new neighbor entities respectively, and constructing a snapshot graph according to the new transaction nodes, the transaction time corresponding to the new transaction nodes, the new neighbor entities and the reference transaction nodes; determining a first target directed graph based on the target static graph and the snapshot graph, and training the target graph neural network by using the first target directed graph to obtain a trained neural network; and when a new transaction occurs, determining a second target directed graph of the new transaction through transaction information of the new transaction, determining a risk score of the new transaction by using the trained neural network and the second target directed graph, and determining a fraud risk of the new transaction based on the risk score. Therefore, accurate fraud monitoring is realized.

The described technology relates to a deep learning based method and system for detecting abnormal cryptocurrency transaction between computing devices in a blockchain network. In once aspect, the method includes generating, at a server, a first data set, a second data set, and a third data set from the transactions of a user wallet address with at least one other user wallet address. The method may also include running a pre-learned deep learning module to extract a first feature vector, a second feature vector, and a third feature vector from the first data set, the second data set, and the third data set. The method may further include converting the first feature vector, the second feature vector, and the third feature vector into an intermediate value and comparing the intermediate value to a predetermined reference value to determine if a fraudulent transaction associated with the user wallet address has occurred.

The present invention is the method and system of using biometrics in consumer credit bureau identity matching. The matching function connects a cryptographic template (CT) to a record of information that we call a consumer credit bureau. This invention ensures accuracy and validity of the matching and results in accurate data transaction processing. An advantage of the present invention is the use of CTs as a part of the consumer reporting match process. Introducing the use of biometrics shifts the reliance on traditional personally identifiable information (PII) to attributes that are a physical / integral part of the person. This moves us away from relying on data elements that are not attached to a person and results in improved accuracy of matching. The improved accuracy of matching serves to decrease loss due to failed or fraudulent transactions.

A computer implemented method of training a model, using a machine learning process, to predict whether a transaction of a digital currency stored in a blockchain is fraudulent, comprises: unpacking (202) a block in the blockchain into a table comprising one or more rows of input and output data for a previous transaction stored in the block and aggregating (204) the one or more rows of input and output data to form an aggregated row of transaction data for the previous transaction. The method further comprises labelling (206) the aggregated row of transaction data for the previous transaction according to whether the previous transaction was fraudulent and using (208) the aggregated row of transaction data and the label as training data with which to train the model.

A verified real estate registration system, where individuals may register, for avoiding a fraudulent transaction occurring with respect to real property. During registration, basic information identifying the individual or entity registering along with verifying documentation are entered into the system. Once the identity of the individual or entity is confirmed, the individual or entity then becomes a registered user of the system and is permitted to enter any real property, solely or jointly owned by the registered user, into a data base of the system. The system also permits registered users to generate a warning notice, with respect to real property, which is recordable in a chain of title of any real property, owned by the registered individual or entity, so as to avoid any fraudulent transfer occurring respect to the real property having the warning notice recorded in the chain of title.

The invention relates to a UnionPay payment terminal security algorithm verification method and system, and the method comprises the steps: carrying out the behavior monitoring of a payment terminal, and inputting the environment recognition security of a preset user when detecting that the preset user inputs a bank card number; after format verification is carried out on the input card number of the preset user, legality verification is carried out on the Hash value of the input card number, an identity verification stage is entered, and whether the identity of the card holder is matched with that of the preset user is confirmed through at least two combined verification modes based on the information of the bank card number; after identity matching is completed, entering a transaction fraud behavior identification stage, and identifying whether a current transaction has a fraud behavior or not through technical means such as machine learning or a rule engine and the like based on current transaction information of a preset user and a behavior identification model; and if the current transaction of the preset user is identified as normal, continuing to complete the payment process, so as to achieve the purposes of enhancing the tamper-proof and non-repudiation capabilities of transaction information and improving fraudulent transaction prevention.

Systems and processes are disclosed for real-time fraud detection and prevention in in-person transactions. The invention utilizes an AI / ML engine to analyze customer application data for inconsistencies and unusual requests indicative of potential fraud. Concurrently, a real-time conversation analysis engine with speech recognition algorithms monitors interactions between bank associates and customers, identifying suspicious speech patterns, hesitations, and keywords associated with scams. By combining insights from application data and conversational analysis, the system generates a comprehensive risk assessment. When a high probability of fraud is detected, an alert notifies the bank associate, security personnel, and other relevant individuals. This proactive approach enables immediate action to prevent fraudulent transactions, reducing manipulation risks and minimizing financial losses. The system continuously learns from new data, adapting to evolving fraud tactics, thus providing robust, long-term protection for financial institutions and their customers.

Systems and methods discussed herein relate to a system for detecting and preventing fraudulent transactions through collecting requests for website access, stored-card trades and sales, and transactions using physical or electronically issued credit, debit, stored-value, and other cards. The fraud detection system not only flags individual transactions, but is also configured to dynamically track and update banned / watch lists associated with request artifacts and cards in order to catch and prevent individual actors as well as BOTs, at least by adjusting the thresholds used to evaluate risk scores and what is placed on the banned / watch lists based upon requests received as well as information from financial institutions, merchants, government agencies, and watchdog groups. The system is also configured to provide information to those groups.

A method for detecting fraudulent transactions may include receiving transaction data for a plurality of transactions, assigning a unique identifier to each transaction, sorting the plurality of transactions into a first plurality of groups of transactions by a first sorting parameter, assigning the unique identifier of a first transaction within each group to all other transactions within the respective group, sorting the plurality of transactions into a second plurality of groups of transactions by a second sorting parameter, assigning the unique identifier of a second transaction within each group to all other transactions within the respective group, determining whether each group of transactions is a sequence of fraudulent transactions, and upon determining that a group of transactions is a sequence of fraudulent transactions, marking each transaction among the determined group of transactions as potentially fraudulent.

A system and method including: obtaining pending transactions; processing weightings for parameters associated with the pending transactions; encoding the pending transactions with the processed weightings into corresponding transaction vector representations; obtaining nearest transactions using the transaction vector representations; determining whether the pending transactions are flagged for association with at least one risky entity; in response to determining that the pending transactions are flagged for association with a risky entity, generating an alert for the nearest transactions; in response to determining that the pending transactions are not flagged, determining whether the nearest transactions are associated with at least one risky entity; in response to determining that the pending transactions or the nearest transactions are flagged for association with at least one risky entity, generating an alert for the pending transactions; and output the generated alerts to one or more computing devices via a network interface.

A fraud detection system to proactively identify fraudulent transactions. The system receives an analyst-generated report for a transaction and trains a model using features from the feature types identified in analyst-generated reports. The system generates a fraud likelihood score for each requested transaction, compares it to the analyst-generate assessment, and retrains the model based on the difference.

The present invention discloses a financial system monitoring system based on blockchain and cloud computing, belonging to the field of financial system monitoring technology. The system includes a data acquisition module, a data analysis module, a data processing module, a monitoring module, a prediction module, and a blockchain storage module; the output of the data acquisition module is connected to the input of the data analysis module, the monitoring module, and the blockchain storage module; the output of the data analysis module is connected to the input of the data processing module; the output of the data processing module is connected to the input of the monitoring module and the prediction module; and the output of the monitoring module is connected to the input of the blockchain storage module. The system can effectively prevent the occurrence of fraudulent transactions, coerced transactions, and other undesirable behaviors during financial transactions, and all data is uploaded to the blockchain storage module, further enhancing traceability and effectively preventing data loss.

This application relates to apparatus and methods for identifying fraudulent transactions. The embodiments may employ machine learning processes to detect fraudulent activity. In some examples, a computing device determines customer data and device data for a customer and device involved in a transaction. The customer data may include previous transactions by the customer, and the device data may include previous transactions involving the device. The computing device generates features based on the customer data and the device data, and applies one or more machine learning models to the generated features to generate a trust score. The trust score is indicative of how likely a transaction is to be fraudulent. In some examples, the transaction is not allowed if the trust score is beyond a threshold. In some examples, the computing device trains the machine learning models based on customer data and device data for a plurality of customers and devices.

The invention discloses a fraud risk prediction method and device in combination with historical behavior analysis, and relates to the technical field of transaction risk prediction.The method comprises the steps that when a user initiates a transaction, transaction state information is monitored, and the transaction state information comprises transaction behaviors, network behaviors and biological behaviors; counting accounts whose transaction frequency with the user is greater than or equal to a threshold value, and obtaining a user associated account set; if there is no risk account in the associated account set (i.e., the historical fraud frequency is lower than a threshold value), counting a fraud transaction proportion in historical transaction behaviors having the same modality as the transaction behavior and the network behavior; if the fraudulent transaction proportion is lower than a threshold value, counting historical biological behaviors of the user; and if the historical biological behavior is inconsistent with the current biological behavior, generating a fraud risk prompt and feeding back the fraud risk prompt to the user side, thereby achieving the technical effect of improving the accuracy and reliability of fraud risk prediction.