Abstract: Systems and methods for dynamic data operations modeling. A system may include a processor and a memory storing processor-executable instructions that configure the processor to: retrieve, at time stages, a set of data records associated with meta attributes representing operations on the data records at stages over time; generate, at the respective time stages, a categorical prediction associated with the data operations based on a detection model and a set of meta attributes associated with the retrieved data records, the detection model based on a multinomial logistic regression providing the categorical prediction for adapting multi-label predictions; and transmit, following the successive time stages, one or more signals representing the categorical prediction for dynamically updating the user interface for communicating an interim categorical prediction during data operations execution.

Abstract: Risk levels for accounts are assessed based on account data for the accounts, including reconciliation status and balance values. For each of the accounts, risk criteria scores are assigned for a plurality of risk criteria, based at least in part on one or more of reconciliation status and balance value for the account, and a composite risk score is determined for the account based on a weighted summation of a selection of the risk criteria scores for the account. A density distribution of the composite risk scores is generated for an audit population of the accounts, and quantiles determined. Thresholds are identified based at least in part on the quantiles of the density distribution, and a risk rating assigned to each of the accounts in the audit population based at least in part on the composite risk score for that account as compared to the thresholds.

Abstract: Systems and methods of monitoring for anomalous data records. The system conducts a method including: receiving a data record associated with at least one meta attribute to determine whether subsequent processing of the data record is warranted; generating an anomaly prediction for the data record based on a detection model and the at least one meta attribute associated with the data record, the detection model defined by a plurality of score distribution representations based on quantile bins and a dynamic quantile weight for providing an interim anomaly measure corresponding to respective score distribution representations, wherein the anomaly prediction is generated based on a combination of interim anomaly measures associated with respective meta attributes associated with the data record; and transmitting a signal representing the anomaly prediction for presentation at a user device for identifying one or more data records for subsequent data processes.