Bank of America Corporation

Venkata Durga Rao Medisetti

Vinay Jaisinghani

Siju Abraham

Naresh Soni

Barnali Sanyal

Samson Paulraj

A system is provided for resolving data errors. The system is configured to receive a data element from a source application and process the data element using an Extract, Transform and Load (ETL) logic. Upon detecting that the processing of the data element has failed as a result of an error in the data element, the system generates a plurality of probable data values for the data element. For each probable data value of the plurality of probable data values, the system verifies whether the probable data value can be processed successfully by the ETL logic and by one or more target applications configured to process the data element. The system determines one of the probable data values as a suggested data value of the data element, based on the verification of the probable data values and processes the suggested data value using the ETL logic.

Recovering from data processing errors by data error detection and correction

A system is provided for resolving data errors. The system is configured to obtain an erroneous data element received from a source system and generate a plurality of probable data values for the data element. For each probable data value of the plurality of probable data values, the system verifies whether the probable data value can be processed successfully by an Extract, Transform and Load (ETL) logic and by one or more target applications configured to process the data element. The system determines one of the probable data values as a suggested data value of the data element, based on the verification of the probable data values. The system outputs the suggested data value for processing by the ETL logic.

Correcting data errors for data processing fault recovery

Aspects of the disclosure relate to dynamic transformation code prediction and generation for an unavailable data element. In some embodiments, a computing platform may execute an extract, transform, and load process on input data received from a plurality of data sources and detect a missing data element in the received input data. Subsequently, the computing platform may generate a prediction model with respect to the missing data element, which may include executing a first, second, and third machine learning algorithm. Next, the computing platform may determine a confidence level of the prediction model. In response to determining that the confidence level is at or above the predetermined threshold, the computing platform may generate executable transformation code implementing the prediction model. Thereafter, the computing platform may monitor transformation code implementations and execute a fourth machine learning algorithm to adjust the prediction model based on the transformation code implementations.

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Hyderabad Telangana, India

Venkata Durga Rao Medisetti