66 results about "Fraudulent transaction" patented technology

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.

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.

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 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.

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.

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.

The invention discloses an unbalanced fraud detection method based on WGAN-GP oversampling, and relates to the technical field of information security detection, and the method comprises the following steps: judging an unbalanced transaction data set, if the number of fraud transactions is equal to the number of normal transactions, outputting the unbalanced transaction data set as a balanced transaction data set, and if the number of fraud transactions is equal to the number of normal transactions, outputting the balanced transaction data set; otherwise, calculating the information value of the unbalanced transaction data set, training the WGAN-GP based on the information value, and generating a fraudulent transaction sample; training a strong classifier through an ensemble learning method, calculating a judgment condition for judging that a sample is a fraudulent transaction, carrying out fraudulent transaction integrated screening on the fraudulent transaction, supplementing qualified generated fraudulent transactions into an unbalanced transaction data set, repeating the operation until the number of the fraudulent transactions is equal to the number of normal transactions, and outputting a balanced transaction data set; noise is effectively suppressed while high-quality fraud samples are generated, and the precision and generalization ability of unbalanced fraud detection are remarkably improved.

The invention provides a payment fraud real-time identification and disposal method and system based on machine learning, and relates to the technical field of computers, and the method comprises the steps: constructing a transaction relation graph, recognizing an abnormal transaction mode through a graph attention network, and calculating a transaction suspicious degree score; and when the preset threshold value is exceeded, triggering a self-adaptive verification strategy based on the knowledge graph and reinforcement learning, executing a corresponding verification means and processing a transaction result. According to the invention, accurate identification and efficient disposal of fraudulent transactions can be realized, the payment security is improved, the error interception rate is reduced, and the user experience is optimized.

Systems and methods for evaluating risks for physical locations are disclosed. A request for risk evaluation of transactions associated with a physical location located within a predetermined region including a plurality of neighboring locations is received. A spatial model is implemented to generate a first risk score based on chargeback data associated with the physical location and chargeback data of the plurality of neighboring locations. A temporal model is implemented to generate a second risk score for the physical location. A final risk score for the physical location is generated based on the first risk score and the second risk score. The final risk score is transmitted to a computing device for detecting fraudulent transactions associated with the physical location in response to the request.

The invention discloses a fraudulent transaction detection method and device, equipment and a medium, which are used for improving the understanding capability of a transaction detection model on transaction data, so that the fraudulent transaction detection effect is improved, and the situations of missing detection, false alarm and the like are avoided. The fraudulent transaction detection method provided by the invention is suitable for the field of bank card payment, and the method comprises the steps: obtaining original transaction data; wherein the original transaction data is presented in the form of coded fields, and comprises a plurality of coded fields of at least one type; based on a preset domain knowledge base, converting the original transaction data into a transaction text described by adopting a natural language; wherein the domain knowledge base comprises a corresponding relation between a coding field and a semantic text described by adopting a natural language; and based on the transaction text described by adopting the natural language, adopting at least one preset transaction detection model to carry out fraudulent transaction detection.

Methods, systems, and apparatuses are described herein for improving computer authentication processes through excluding fraudulent transactions in transaction-based authentication. A computing device may receive a request for access to an account from a user. The computing device may provide transaction data to a machine learning model. The computing device may receive data indicating a confidence threshold associated with the user from the machine learning model. The computing device may generate a modified set of merchant choices for the user by excluding merchants related to potentially fraudulent transactions within a predetermined time period. An authentication question may be generated, and access to the account may be provided based on a response to the authentication question.

Methods, systems, and apparatuses are described herein for improving computer authentication processes through excluding fraudulent transactions in transaction-based authentication. A computing device may receive a request for access to an account from a user. The computing device may provide transaction data to a machine learning model. The computing device may receive data indicating a confidence threshold associated with the user from the machine learning model. The computing device may generate a modified set of merchant choices for the user by excluding merchants related to potentially fraudulent transactions within a predetermined time period. An authentication question may be generated, and access to the account may be provided based on a response to the authentication question.

The invention provides a promotion income sharing method, and relates to the field of intelligent wardrobes, and the method comprises the steps: responding to an exchange request of a digital exchange coupon of a user side, and generating an encryption certificate containing an effective timestamp and a unique identifier based on a first node; when a corresponding target device identifies the digital exchange coupon, the encrypted certificate is verified through a second node; if the encrypted certificate is in an unused state and is valid, controlling the target device to execute an exchange request of the digital exchange certificate, determining a promotion income amount corresponding to the exchange request, and writing corresponding verification success information and the promotion income amount into the block chain; and if the encryption certificate is in the expired state and / or the used state, writing verification failure information of the encryption certificate into the block chain. By means of block chain distributed verification and non-tampering characteristics, the states of the exchange coupons are clearly distinguished, the problem of state fuzziness is solved, it is ensured that the basis for promotion income calculation is real and reliable, and fraudulent transactions are reduced.

Apparatus and methods for an opt-in distributed ledger consortium to detect and prevent fraudulent transactions are provided. Two or more entities may opt into a private distributed ledger. A program may receive financial transaction information. The program may record the transaction on the distributed ledger. The program may activate a smart application on the ledger. The smart application may analyze each transaction for indicators of illegal activity. When indicators of illegal activity are found within a transaction, the program may generate a report. The program may transmit the report to each entity that has opted into the private distributed ledger. The program may also record the report on the private distributed ledger.

A computing platform is configured to (i) identify a candidate set of card-not-present (CNP) transactions that are candidates for potential involvement in fraudulent activity, (ii) for each respective CNP transaction in the candidate set, determine a respective combination of transaction-element values for a set of transaction elements comprising at least (a) a first transaction element indicating a Bank Identification Number (BIN) number of a respective PAN involved in the respective CNP transaction, (b) a second transaction element indicating a client involved in the respective CNP transaction, and (c) a third transaction element indicating a merchant involved in the respective CNP transaction, (iii) based on an evaluation of the respective combinations of transaction-element values determined for the CNP transactions in the candidate set, identify at least one at-risk combination of transaction-element values that is associated with a risk of fraudulent activity, and (iv) use the identified at least one at-risk combination of transaction-element values as a basis for deploying logic for identifying CNP transactions that present a risk of fraudulent activity.

A computing platform is configured to (i) identify a candidate set of card-not-present (CNP) transactions that are candidates for potential involvement in fraudulent activity, (ii) for each respective CNP transaction in the candidate set, determine a respective combination of transaction-element values for a set of transaction elements comprising at least (a) a first transaction element indicating a Bank Identification Number (BIN) number of a respective PAN involved in the respective CNP transaction, (b) a second transaction element indicating a client involved in the respective CNP transaction, and (c) a third transaction element indicating a merchant involved in the respective CNP transaction, (iii) based on an evaluation of the respective combinations of transaction-element values determined for the CNP transactions in the candidate set, identify at least one at-risk combination of transaction-element values that is associated with a risk of fraudulent activity, and (iv) use the identified at least one at-risk combination of transaction-element values as a basis for deploying logic for identifying CNP transactions that present a risk of fraudulent activity.

This invention provides a machine learning-based method and system for real-time identification and handling of payment fraud, relating to the field of computer technology. The method includes constructing a transaction relationship graph, using graph attention networks to identify abnormal transaction patterns, and calculating transaction suspiciousness scores. When a preset threshold is exceeded, an adaptive verification strategy based on knowledge graphs and reinforcement learning is triggered to execute corresponding verification measures and process the transaction results. This invention enables accurate identification and efficient handling of fraudulent transactions, improves payment security, reduces false interception rates, and optimizes user experience.

In point-to-point payment, fraudulent transactions are detected without an intermediary. An audit trace associated with the transaction chain is generated for transferring the digital currency from the initial sending wallet to the final receiving wallet and associating the audit trace with the token of the digital currency. Before being received by the final receiving wallet, each token maintains an audit trace of an intermediate digital wallet in which the token is transacted. A request is received to identify whether a first transaction in a transaction chain is a fraudulent transaction. In response to the request, a first audit trace associated with a first token associated with the first transaction is retrieved. The first transaction is determined as a fraudulent transaction when a wallet clone or value tampering is detected in a first token associated with the first transaction by analyzing the first audit trace.

This will prevent fraudulent transactions by third parties using automated trading systems. [Solution] The ATM 100, which performs transactions in response to user operations, includes a transaction detection unit 220 that detects transaction operations for the user to perform a transaction, an image acquisition unit 230 that acquires an image of the user when a transaction operation is detected, an estimation unit 240 that analyzes the user's image and estimates the user's attribute information, a storage unit 210 that stores pre-registered user attribute information, a determination unit 250 that compares the estimated user attribute information with the pre-registered user attribute information and determines whether or not to perform a transaction in response to the user's operation based on the comparison result, and an output unit 260 that outputs the determination result.

Described are a system, method, and computer program product for early detection of and response to a merchant data breach through machine-learning analysis. The method includes receiving transaction data associated with a plurality of transactions and receiving fraudulent transaction data representative of at least one previously identified data-breach incident. The method also includes generating a first model input dataset associated with the at least one merchant and a second model input dataset associated with the at least one previously identified data-breach incident. The method also includes training at least one machine-learning prediction model to associate merchants with a likelihood of data breach and determining at least one breached merchant of the at least one merchant. The method further includes generating a communication configured to cause at least one action to be taken in response to the determination of the at least one breached merchant.

The invention discloses a transaction fraud detection method, system and device and a storage medium, belongs to the field of financial fraud detection, and solves the technical problem that in the prior art, when transaction data with high dimensionality, small samples and unbalanced extreme categories are adopted, only shallow combination of homogeneous integration and simple migration is adopted, and performance improvement in a cross-domain scene is limited. Obtaining a public credit card fraud data set, pre-training the deep neural network structure to obtain a feature extractor, and extracting migration features; splicing the migration features and fraudulent transaction detection data features to construct a combined data set; designing a base model layer composed of a plurality of heterogeneous base models to predict the combined data set, and training the meta-model layer based on a prediction result to obtain a TEL integrated model; and performing fraud detection on the transaction data by using the TEL integration model to obtain a fraud detection result. The method is used for realizing high-accuracy and high-stability transaction fraud detection.

A method for optimizing refunds for suspected or detected fraudulent transactions includes receiving a chargeback analysis request for a potential chargeback transaction from a merchant or a payment processor extracting identifying information of transactions associated with the chargeback transaction from the chargeback analysis request, searching for a chargeback analysis profile in a profile database, determining whether the chargeback analysis profile exists in the profile database, upon determining that the chargeback analysis profile does not exist in the profile database, obtaining a new fraud analysis profile, determining, based on the chargeback analysis profile, a first probability that the potential chargeback transaction will result in a chargeback, determining, based on the chargeback analysis profile, a second probability that the potential chargeback transaction will result in a chargeback after a proactive electronic refund transaction, and generating a proactive electronic refund transaction based on the first probability and the second probability.

An automated tracking system for a pharmacy that tracks unique identifiers (“UI”) with a stock supply of an item or items within a supply container and that provides a security system for determining a discrepancy associated with the stock supply. The UI information automatically travels with the stock supply until it is dispensed to a customer or patient, thereby allowing the system to monitor and track supply for replenishment of the supply or improper or potentially fraudulent transactions. If desired, the improved security allows supply containers and individual supplies within those containers to be stored in the same storage area thereby maximizing available storage space while still improving dispensing and filling accuracy and minimizing loss.

Hellinger decision tree for fraud detection [Solution] The Hellinger decision tree can detect fraudulent transactions in a dataset of financial transactions. The Hellinger distance is used to apply the Hellinger decision tree. The Hellinger decision tree can be part of a machine learning algorithm. In one example, the Hellinger decision tree is a positive-and-unbalanced Hellinger decision tree used with unbalanced positive examples and unlabeled data.

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, the method comprising: unpacking (202) a block in the blockchain into a table comprising one or more rows of input and output data of 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 a previous transaction. The method further includes tagging (206) an aggregated line of transaction data for the previous transaction according to whether the previous transaction is fraudulent, and using (208) the aggregated line of transaction data and the tag as training data for training the model.

Fraudulent transactions are detected in peer-to-peer payments without intermediaries. An audit trail is generated associated with the chain of transactions transferring digital currency from the initial sending wallet to the final receiving wallet, and is also associated with the token of the digital currency. Each token maintains an audit trail of the intermediate digital wallets it passed through in the transaction before being received by the final receiving wallet. A request is received to identify whether a first transaction is a fraudulent transaction. In response to the request, the first audit trail associated with the first token associated with the first transaction is retrieved. If, by analyzing the first audit trail, wallet cloning or value manipulation is detected within the first token associated with the first transaction, the first transaction is determined to be a fraudulent transaction.

The application provides a fraud transaction account detection method based on a heterogeneous graph convolution network, which comprises the following steps: constructing a bipartite graph by taking a plurality of target transaction accounts as account nodes, taking a plurality of orders associated with each target transaction account as order nodes, and taking the association relationship between each target transaction account and each order associated with the target transaction account as an order edge; wherein the order edge comprises an order submission edge and an order cancellation edge; performing convolution processing on the bipartite graph based on the heterogeneous graph convolution network to obtain an account node hidden state corresponding to the account node, an order submission edge hidden state and an order cancellation edge hidden state; and inputting the account node hidden state, the order submission edge hidden state and the order cancellation edge hidden state into a pre-acquired classification model to make the classification model output account anomaly detection result data of the account node. The application can effectively improve the accuracy and detection efficiency of transaction fraud account detection.

An object of a system according to an embodiment is to analyze transaction data, detect a transaction having a possibility of fraud, and warn a user.SOLUTION: A system includes a transaction data collection part, an analysis part, and a warning part. The transaction data collection unit collects transaction data. The analysis unit analyzes the transaction data collected by the transaction data collection unit and detects a transaction that may be a fraud. The alert unit issues an alert message to the user based on the potentially fraudulent transaction detected by the analysis unit.SELECTED DRAWING: Figure 1