Abstract: Embodiments include predicting transactions by an entity and identifying promotions to offer the entity. Aspects include parsing a plurality of event records corresponding to a plurality of entities respectively. Aspects also include identifying a sequence of events corresponding to the entity and discretizing time durations and event values of the sequence of events into discrete symbolic values. Aspects further include generating a temporal pattern of events in the sequence of events, the temporal pattern including a sequence of transaction-symbols representative of the time duration and the event value of the events in the sequence of events of the entity and predicting a next transaction based on the temporal pattern.

Abstract: Embodiments include predicting transactions by an entity and identifying promotions to offer the entity. Aspects include parsing a plurality of event records corresponding to a plurality of entities respectively. Aspects also include identifying a sequence of events corresponding to the entity and discretizing time intervals and event values of the sequence of events into discrete symbolic values. Aspects further include generating a temporal pattern of events in the sequence of events, the temporal pattern including a sequence of transaction-symbols representative of the time interval and the event value of the events in the sequence of events of the entity and predicting a next transaction based on the temporal pattern.

Abstract: An aspect of post-lending credit management includes collecting data associated with events for a plurality of clients, serializing the data by time stamp and value to produce a client-based time series of the events, and performing feature generalization for the time series. Feature generation includes grouping the client-based time series according to a selected feature to produce feature-based time series, defining a feature-based default burst and a threshold value for the feature-based time series, identifying a point in time on the feature-based time series when the feature-based default burst reaches the threshold value, determining a cause and effect relationship between default events occurring across the plurality of feature-based time series from the feature-based default burst, and outputting feature-based default rules from the corresponding cause and effect relationship.

Abstract: Refining cluster definition: (i) receiving data items, each characterized by values respectively corresponding to a set of dimension(s); (ii) receiving initial cluster identification that divides the set of data items into multiple initial clusters; (iii) determining a distribution curve, with respect to a first dimension, of data items of a first initial cluster; (iv) determining a distribution curve, with respect to the first dimension, of data items of a second initial cluster; and (v) determining a first-dimension-first-cluster-second-cluster cut-off value such that the following two proportions are substantially equal: (a) a proportion of the area under the first distribution curve and below the first-dimension-first-cluster-second-cluster cut-off value to the total area under the first distribution curve, and (b) a proportion of the area under the second distribution curve and above the first-dimension-first-cluster-second-cluster cut-off value to the total area under the second distribution curve.

Abstract: Embodiments include predicting transactions by an entity and identifying promotions to offer the entity. Aspects include parsing a plurality of event records corresponding to a plurality of entities respectively. Aspects also include identifying a sequence of events corresponding to the entity and discretizing time durations and event values of the sequence of events into discrete symbolic values. Aspects further include generating a temporal pattern of events in the sequence of events, the temporal pattern including a sequence of transaction-symbols representative of the time duration and the event value of the events in the sequence of events of the entity and predicting a next transaction based on the temporal pattern.

Abstract: Embodiments include predicting transactions by an entity and identifying promotions to offer the entity. Aspects include parsing a plurality of event records corresponding to a plurality of entities respectively. Aspects also include identifying a sequence of events corresponding to the entity and discretizing time intervals and event values of the sequence of events into discrete symbolic values. Aspects further include generating a temporal pattern of events in the sequence of events, the temporal pattern including a sequence of transaction-symbols representative of the time interval and the event value of the events in the sequence of events of the entity and predicting a next transaction based on the temporal pattern.