Sap Se

Atreya Biswas

Srivatsan Santhanam

Sean Saito

Auguste Byiringiro

Jiatai Qiang

Nirmal Baven

Anmol Bhat

A framework provides a detailed explanation regarding specific aspects of a (complex) calculation produced by an application (e.g., an analytical application). An explainability engine receives a request for explanation of the calculation. The explainability engine traverses homogenous data clusters according to the request, in order to produce a final path. The final path is used to select and then populate a template comprising explanation note(s). The outcome (comprising the final path and the template) is processed with a ruleset according to a covariance (COV) function in order to provide a first intermediate outcome. The first intermediate result is then processed with a second input according to a correlation (COR) function to provide a second intermediate outcome. The second intermediate result is processed according to a challenge function to provide a challenged outcome, and feedback (e.g., reward or penalization) to the ruleset. The challenged outcome provides detailed explanation to the user.

Explanation of computation result using challenge function

Methods, systems, and computer-readable storage media for receiving a first bank statement at a hybrid pipeline including a set of lookup tables and a deep learning (DL) model that can each be used to determine customer IDs from bank statements, providing a first key based on data associated with the first bank statement, and determining that the first key is included in a first lookup table of the set of lookup tables, and in response: identifying a first set of customer IDs from the first lookup table, the first set of customer IDs including one or more customer IDs, and outputting the first set of customer IDs to computer-executable software that matches the first bank statement to one or more electronic documents at least partially based on the first set of customer IDs.

Automated hybrid pipeline for customer identification

Methods, systems, and computer-readable storage media for receiving historical data, the historical data including variable vectors, each variable vector being assigned to a class, processing the historical data through encoders to provide feature vectors, each feature vector corresponding to a respective variable vector and being assigned to the class of the respective variable vector, generating a set of decision trees based on the feature vectors, each decision tree corresponding to a class in the set of classes, transforming each decision tree into a set of rules to provide sets of rules, each rule in a set of rules defining conditions to assign at least a portion of an electronic document to a respective class in the set of classes, and providing the sets of rules for execution in an enterprise system, the enterprise system classifying electronic documents to classes in the set of classes based on the sets of rules.

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Singapore, Singapore

Atreya Biswas