Fair Isaac Corporation

Shane De Zilwa

Scott Michael Zoldi

Larry Peranich

Uwe Mayer

Anthony Vaiciulis

Jeffrey Allen Feinstein

William Groves

Chiung-Chi Wang

Ramya Raghunathan

A computer-implemented method and system for automated entity identification for efficient profiling in an event probability prediction system. A first subset of entities belonging to one or more entity classes is defined. At least one historical profile is constructed for each entity in the subset of entities based on a set of possible outcomes of transaction behavior of each entity in the first subset of entities. Based on the historical profiles, a second subset of entities having transaction behavior associated with a transaction is selected, the transaction behavior being predictive of at least one targeted outcome from the set of possible outcomes. The first subset of entities is redefined with the second subset of entities.

Automated entity identification for efficient profiling in an event probability prediction system

Data characterizing a plurality of sensor generated events is received. Thereafter, analysis of the plurality of events is initiated using a decision tree with splits performed on decision keys. A first portion of the decision keys comprising analyst-selected splits can be derived from user-generated domain knowledge regarding a first plurality of historical events. A second portion of the decision keys comprising software-driven splits can be derived from a predictive model trained using a second plurality of historical events. Later, a disposition is determined for each event based on a traversal of at least one of the decision keys in the decision tree and such disposition is later initiated. Related apparatus, systems, techniques and articles are also described.

Determing a disposition of sensor-based events using decision trees with splits performed on decision keys

A request to generate a consolidation risk score that characterizes a likelihood of a change in a level of creditworthiness of an individual following a consolidation of debt of the individual using a secured line of credit is received. Thereafter, future credit balance increases are estimated for the individual using a predictive model trained using historical creditworthiness data of a plurality of consolidators. These estimated future balance increases are then associated with a consolidation risk score so that such score can be provided. Related apparatus, systems, techniques, and articles are also described.

Modeling responsible consumer debt consolidation behavior

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Oakland, CA, United States of America

Shane De Zilwa