Abstract: Computer vision and deep learning techniques are leveraged to detect behavior patterns in transaction histories. A transaction timeline is built for a series of transactions, e.g., financial, and a graphic image is constructed representing the transaction timeline. The graphic image is then matched to a known behavior pattern using a cognitive system. The cognitive system is trained with historical timeline images having associated labels. In one example the graphic image is a bar chart and each financial transaction is represented as a bar in the bar chart having a height proportional to a transaction amount, the bar being located along a time axis of the bar chart according to the transaction date and being color coded according to the transaction type.

Abstract: The present application relates to systems for updating detection models and methods for using the same. The systems and methods generally comprise at least one local node comprising a monitoring module, a diagnosis module, and an evaluation module The system receives at least one model update, and analyzes the model update and current models and data present in the local node, and determines if the update should be applied. In some embodiments, a local node can generate a model update for use in other local nodes, while not sharing private data present in the local node.

Abstract: Embodiments include systems and methods for tracking real estate transaction information to maintain a searchable and secure real estate immutable record. A records system receives first real estate property information, identifies a real estate immutable record based on the first real estate property information, updates the real estate immutable record with the first real estate property information, creates a first transaction tag associated with updating the real estate immutable record, and distributes the first transaction tag to a plurality of second computing devices. Embodiments may further include relevance tagging for recalling the real estate immutable record for a cognitive advisor system.

Abstract: The present application relates to systems for updating detection models and methods for using the same. The systems and methods generally comprise at least one local node comprising a monitoring module, a diagnosis module, and an evaluation module The system receives at least one model update, and analyzes the model update and current models and data present in the local node, and determines if the update should be applied. In some embodiments, a local node can generate a model update for use in other local nodes, while not sharing private data present in the local node.

Abstract: Embodiments can provide a computer implemented method for identifying a match between a commercial buyer and a seller for a real estate transaction. The method includes receiving, from the buyer, a service request and receiving, from the buyer, historical information stored in a buyer immutable record. The method also includes receiving one or more real estate requirements and one or more commercial external factors and determining a buyer need profile based on the historical information, the real estate requirements, and the commercial external factors. The method also includes receiving one or more answers in response to one or more first questions raised by the processor, refining the buyer need profile based on the one or more answers, identifying a match between the buyer need profile and a real estate profile from the seller, and providing a ranked list of real estate properties and supporting evidence for each real estate property to the buyer.

Abstract: Computer vision and deep learning techniques are leveraged to detect behavior patterns in transaction histories. A transaction timeline is built for a series of transactions, e.g., financial, and a graphic image is constructed representing the transaction timeline. The graphic image is then matched to a known behavior pattern using a cognitive system. The cognitive system is trained with historical timeline images having associated labels. In one example the graphic image is a bar chart and each financial transaction is represented as a bar in the bar chart having a height proportional to a transaction amount, the bar being located along a time axis of the bar chart according to the transaction date and being color coded according to the transaction type.

Abstract: A local node for updating detection models while maintaining data privacy has a sharing module configured to receive instructions for calculating at least one general feature from data stored at the first local node, a retraining module configured to retrain the detection model to include a detection component that uses the instructions, a data collection module configured to collect data comprising customer data and transaction data stored at the first local node, and a performance module. The performance module is configured to determine a value for the at least one general feature from the collected data using the instructions, and trigger a suspicious activity alert based on the determined value and the instructions. The customer data and transaction data are indeterminable from the at least one general feature and the determined value.

Abstract: Embodiments can provide a computer implemented method for identifying a match between a buyer and a seller for a real estate transaction, comprising: receiving, from the buyer, a service request; receiving, from the buyer, historical information stored in a buyer immutable record; receiving, from the buyer, one or more real estate requirements; determining a buyer need profile based on the historical information and the real estate requirements; receiving, from the buyer, one or more answers in response to one or more first questions raised by the processor; refining the buyer need profile based on the one or more answers; identifying a match between the buyer need profile and an available real estate profile from the seller; and providing a ranked list of real estate properties and supporting evidence for each real estate property to the buyer.

Abstract: Embodiments can provide a computer implemented method for identifying a match between a commercial buyer and a seller for a real estate transaction. The method includes receiving, from the buyer, a service request and receiving, from the buyer, historical information stored in a buyer immutable record. The method also includes receiving one or more real estate requirements and one or more commercial external factors and determining a buyer need profile based on the historical information, the real estate requirements, and the commercial external factors. The method also includes receiving one or more answers in response to one or more first questions raised by the processor, refining the buyer need profile based on the one or more answers, identifying a match between the buyer need profile and a real estate profile from the seller, and providing a ranked list of real estate properties and supporting evidence for each real estate property to the buyer.

Abstract: Embodiments can provide a computer implemented method for identifying a match between a buyer and a seller for a real estate transaction, comprising: receiving, from the seller, a service request; receiving, from the seller, historical information stored in a real estate immutable record; receiving, from the seller, one or more real estate property facts; determining a real estate profile based on the historical information and the one or more real estate property facts; receiving, from the seller, one or more answers in response to one or more first questions raised by the processor; refining the real estate profile based on the one or more answers; identifying a match between an available buyer need profile and the real estate profile from the seller; and providing a ranked list of buyer candidates and supporting evidence for each buyer candidate to the seller.

Abstract: Embodiments provide a computer implemented method, including: ingesting, by the processor, a data file including financial transaction data; generating, by the processor, a schema from the financial transaction data; identifying, by the processor, a data type of each data field in the schema; performing, by the processor, feature selection to select a plurality of data fields from the schema; performing, by the processor, data sampling to select candidate rows of the schema based on the selected data fields; clustering, by the processor, the sampled data to different categories; selecting, by the processor, a set of analytical models for risk prediction, wherein each analytical model corresponds to a data type of each selected data field, and each analytical model generates a risk score; and generating, by the processor, a single risk score indicating the fraud risk by combining all the risk scores generated by the set of analytical models.

Abstract: A local node for updating detection models while maintaining data privacy has an aggregation module, a retraining module, an instructions module, and a sharing module. The aggregation module aggregates the data into features that describe the contents of the data. The retraining module retrains the detection model using the features by implementing an algorithm that includes at least one selected feature and a threshold for triggering an activity alert. The instructions module determines instructions for calculating the at least one selected feature from a different collection of data. The sharing module generates a package having the instructions for calculating the at least one selected feature from the different collection of data and the threshold, and transmits the package to a local node for implementation of the retrained detection model with data stored at the local node.

Abstract: A local node for updating detection models while maintaining data privacy has a sharing module configured to receive instructions for calculating at least one general feature from data stored at the first local node, a retraining module configured to retrain the detection model to include a detection component that uses the instructions, a data collection module configured to collect data comprising customer data and transaction data stored at the first local node, and a performance module. The performance module is configured to determine a value for the at least one general feature from the collected data using the instructions, and trigger a suspicious activity alert based on the determined value and the instructions.

Abstract: The present application relates to systems for updating detection models and methods for using the same. The systems and methods generally comprise at least one local node comprising a monitoring module, a diagnosis module, and an evaluation module The system receives at least one model update, and analyzes the model update and current models and data present in the local node, and determines if the update should be applied. In some embodiments, a local node can generate a model update for use in other local nodes, while not sharing private data present in the local node.

Abstract: The present application relates to systems for updating detection models and methods for using the same. The systems and methods generally comprise at least one local node comprising a monitoring module, a diagnosis module, and an evaluation module The system receives at least one model update, and analyzes the model update and current models and data present in the local node, and determines if the update should be applied. In some embodiments, a local node can generate a model update for use in other local nodes, while not sharing private data present in the local node.

Abstract: A local node for updating detection models while maintaining data privacy has an aggregation module, a retraining module, an instructions module, and a sharing module. The aggregation module aggregates the data into features that describe the contents of the data. The retraining module retrains the detection model using the features by implementing an algorithm that includes at least one selected feature and a threshold for triggering an activity alert. The instructions module determines instructions for calculating the at least one selected feature from a different collection of data. The sharing module generates a package having the instructions for calculating the at least one selected feature from the different collection of data and the threshold, and transmits the package to a local node for implementation of the retrained detection model with data stored at the local node.

Abstract: The present application relates to systems for interpreting analytical results and methods for using the same. The systems and methods generally comprise a result interpreter system and input information provided by a user. The system generates one or more reports that use analytical and visual information to interpreting the analytical result to the user.

Abstract: The present application relates to systems far interpreting analytical results and methods for using the same. The systems and methods generally comprise a result interpreter system and input information provided by a user. The system generates one or more reports that use analytical and visual information to interpreting the analytical result to the user.

Abstract: A cognitive system relies on a pattern library having pattern images with known risk scores to detect potential fraud. Each pattern image begins with a Petri-net model for historical events. A state space representation is generated based on the Petri-net model, and an event pattern layer is established using event sequence vectors from the state space representation. An aggregator layer is also established. The pattern image is created from the event pattern layer and aggregator layer, while applying iterative clustering on the vectors to combine similarities into patterns. A risk score is assigned using supervised or unsupervised learning. The same methodology is used to generate a current pattern image for current events to be analyzed. The cognitive system provides a current risk score based on risk scores associated with likely matches (not necessarily exact) from the pattern library. If the current risk score exceeds a threshold, appropriate action is taken.

Abstract: A cognitive system relies on a pattern library having pattern images with known risk scores to detect potential fraud. Each pattern image begins with a Petri-net model for historical events. A state space representation is generated based on the Petri-net model, and an event pattern layer is established using event sequence vectors from the state space representation. An aggregator layer is also established. The pattern image is created from the event pattern layer and aggregator layer, while applying iterative clustering on the vectors to combine similarities into patterns. A risk score is assigned using supervised or unsupervised learning. The same methodology is used to generate a current pattern image for current events to be analyzed. The cognitive system provides a current risk score based on risk scores associated with likely matches (not necessarily exact) from the pattern library. If the current risk score exceeds a threshold, appropriate action is taken.