Abstract: A system and method for detecting billing errors using predictive models is provided. The system includes a computer system and a billing error detection engine capable of detecting billing errors using predictive modeling techniques. The system receives and pre-processes billing information. The system then applies one or more predictive models to the information to identify billing errors. The results could be optionally sent to, and reviewed by, third party auditors, whereby their feedback could be incorporated into the results. A final report is generated by the system which indicates billing errors that require correction, thereby allowing an entity to correct such errors and prevent revenue leakage.

Abstract: An adaptive method for estimating the selling price for an item at auction in order to set a reserve. The method calculates the selling price as a function of selling prices for items previously-sold at auction and differential values attributable to feature differences between the item to be sold and comparative items previously sold. Distance metrics are calculated by comparing the item to be sold with each item in the set of comparative items, and a subset of most similar items is selected according to the calculated distance metrics. A weighting function is then calculated for each item in the subset based on its respective distance metric value, and the selling price is estimated as a function of the weighting functions and the differential values. The differential values are modeled as a linear dynamical system and updated using a Kalman filter as a function of an actual sales price for the item to be sold and a current estimate of uncertainty for the differential values.

Abstract: A system and method for recommending items to a user is provided. The system could combine recommendations provided by multiple recommenders by: a) calculating for each recommender j a maximum score Pj for the recommended n items as a function (e.g., sum) of stored recommender ratings for the n items, b) calculating a minimum acceptable score for each recommender system j as a function of the maximum score Pj and a predetermined tradeoff factor ?j such that the minimum acceptable score for at least one recommender system j is less than the maximum score Pj, c) selecting at least one set of items from the plurality of items, such that scores Pj (and/or sum of scores Pj) calculated for the selected set of items for each recommender system j are greater than the respective minimum acceptable score for that recommender system j, and d) identifying selected set of items to the user.

Abstract: A system and method for detecting billing errors using predictive models is provided. The system includes a computer system and a billing error detection engine capable of detecting billing errors using predictive modeling techniques. The system receives and pre-processes billing information. The system then applies one or more predictive models to the information to identify billing errors. The results could be optionally sent to, and reviewed by, third party auditors, whereby their feedback could be incorporated into the results. A final report is generated by the system which indicates billing errors that require correction, thereby allowing an entity to correct such errors and prevent revenue leakage.

Abstract: A method for allocating vehicles to auction sites. An inventory database stores information uniquely identifying a plurality of items and associating each of the items with one of a plurality of available auction sites. Constraints relating to the movement of the items are stored in a constraints database. Items are identified which are available to be moved from a current location to one of the auction sites. A profit value is estimated for each available move. A move is identified for one item having a highest estimated profit above an identified threshold and meeting related constraints. This move is selected, and profits are reestimated for remaining items to be moved. The selection steps are repeated until no remaining available moves are feasible according to the threshold and constraints. Once all feasible moves have been selected, information associated with the items and moves is transmitted to a client device.

Abstract: An adaptive method for estimating the selling price for an item at auction in order to set a reserve. The method calculates the selling price as a function of selling prices for items previously-sold at auction and differential values attributable to feature differences between the item to be sold and comparative items previously sold. Distance metrics are calculated by comparing the item to be sold with each item in the set of comparative items, and a subset of most similar items is selected according to the calculated distance metrics. A weighting function is then calculated for each item in the subset based on its respective distance metric value, and the selling price is estimated as a function of the weighting functions and the differential values. The differential values are modeled as a linear dynamical system and updated using a Kalman filter as a function of an actual sales price for the item to be sold and a current estimate of uncertainty for the differential values.

Abstract: Embodiments include systems and methods of detecting fraud. In particular, one embodiment includes a system and method of detecting fraud in mortgage applications. For example, one embodiment includes a computerized method of detecting fraud that includes receiving mortgage data associated with an applicant and at least one entity related to processing of the mortgage data, determining a first score for the mortgage data based at least partly on a first model that is based on data from a plurality of historical mortgage transactions associated with the entity, and generating data indicative of fraud based at least partly on the first score. Other embodiments include systems and method of generating models for use in fraud detection systems.

Abstract: Embodiments include systems and methods of detecting fraud. In particular, one embodiment includes a system and method of detecting fraud in transaction data such as payment card transaction data. For example, one embodiment includes a computerized method of detecting that comprises receiving data associated with a financial transaction and at least one transacting entity, wherein the data associated with the transacting entity comprises at least a portion of each of a plurality of historical transactions of the transacting entity, applying the data to at least one first model, generating a score based on the first model, and generating data indicative of fraud based at least partly on the score. Other embodiments include systems and methods of generating models for use in fraud detection systems.

Abstract: Embodiments include systems and methods of detecting fraud. In particular, one embodiment includes a system and method of detecting fraud in transaction data such as payment card transaction data. For example, one embodiment includes a computerized method of detecting that comprises receiving data associated with a financial transaction and at least one transacting entity, wherein the data associated with the transacting entity comprises at least a portion of each of a plurality of historical transactions of the transacting entity, applying the data to at least one first model, generating a score based on the first model, and generating data indicative of fraud based at least partly on the score. Other embodiments include systems and methods of generating models for use in fraud detection systems.

Abstract: Embodiments include systems and methods of detecting fraud. In particular, one embodiment includes a system and method of detecting fraud in mortgage applications. For example, one embodiment includes a computerized method of detecting fraud that includes receiving mortgage data associated with an applicant and at least one entity related to processing of the mortgage data, determining a first score for the mortgage data based at least partly on a first model that is based on data from a plurality of historical mortgage transactions associated with the entity, and generating data indicative of fraud based at least partly on the first score. Other embodiments include systems and method of generating models for use in fraud detection systems.

Abstract: Embodiments include systems and methods of detecting fraud. In particular, one embodiment includes a system and method of detecting fraud in mortgage applications. For example, one embodiment includes a computerized method of detecting fraud that includes receiving mortgage data associated with an applicant and at least one entity related to processing of the mortgage data, determining a first score for the mortgage data based at least partly on a first model that is based on data from a plurality of historical mortgage transactions associated with the entity, and generating data indicative of fraud based at least partly on the first score. Other embodiments include systems and method of generating models for use in fraud detection systems.

Abstract: A computerized method includes analyzing data associated with a credit line during an origination stage for predictive variables for use in a model for first party fraud, and flagging an account during the origination stage when at least one or more predictive origination stage variables cause a model score to exceed a pre-defined fraud likelihood threshold. The computerized method also includes analyzing data associated with one or more previously flagged, post-booked stage credit lines for data elements or transactions to be used as variables in a model to predictive of first party fraud at the customer-level or in one or more of the post-booked stage credit lines.

Abstract: Embodiments include systems and methods of detecting fraud. In particular, one embodiment includes a system and method of detecting fraud in mortgage applications. For example, one embodiment includes a computerized method of detecting fraud that includes receiving mortgage data associated with an applicant and at least one entity related to processing of the mortgage data, determining a first score for the mortgage data based at least partly on a first model that is based on data from a plurality of historical mortgage transactions associated with the entity, and generating data indicative of fraud based at least partly on the first score. Other embodiments include systems and method of generating models for use in fraud detection systems.

Abstract: Embodiments include systems and methods of detecting fraud. In particular, one embodiment includes a system and method of detecting fraud in transaction data such as payment card transaction data. For example, one embodiment includes a computerized method of detecting that comprises receiving data associated with a financial transaction and at least one transacting entity, wherein the data associated with the transacting entity comprises at least a portion of each of a plurality of historical transactions of the transacting entity, applying the data to at least one first model, generating a score based on the first model, and generating data indicative of fraud based at least partly on the score. Other embodiments include systems and methods of generating models for use in fraud detection systems.