Accenture Global Solutions Limited

David Clune

Chung-Sheng Li

Emmanuel Munguia Tapia

Jingyun Fan

Mohammad Jawad Ghorbani

Priyankar Bhowal

Sumraat Singh

Abhishek Gunjan

Samar Alam

Data from multiple sources may be gathered continuously to perform reconciliation operations. The data items in a first data set may be matched with those in the second data set using a data matching technique. Based on the matching, a confidence score indicative of an extent of match between the data items in the data sets may be generated. Based on the confidence score and predefined thresholds, it may be ascertained if the data items are reconciled. The non-reconciled items in at least one of the first data set and the second data set may be classified in a classification category, based on an artificial intelligence based technique, the classification category being indicative of an explanation of a non-reconciled data item being non-reconcilable. When the data item is not reconciled and classified, the data item is identified as an open item for further analysis.

Data reconciliation

In an example, data, such as, a journal entry in a ledger, to be validated and associated supporting documents may be extracted. Further, an entity, indicative of a feature of the data may be extracted. Based on the extracted entity, one or more probable values for a field of the data may be determined. A probability score may be associated each of the probable values of the field. At least one of the probable values of the field may be compared with an actual value of the field of the data. Based on comparison, a notification indicative of a potential error in the data may generated. The data and historical data associated with the data may be processed, based on at least one of predefined rules and a machine learning technique, to detect an anomaly in the data, the anomaly being related to a contextual information associated with the data.

Data validation

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David Clune