Abstract: A method and system are provided to infer and use machine learning algorithms that is applied to financial data and also to determine: (i) compliance with the applied algorithms, and (ii) whether any exceptions to and/or variations from the algorithms are within an organization's norms. A method includes: receiving financial data; applying a set of dynamic or interdependent algorithms to the received financial data to obtain a set of outcomes; and using a machine learning classifier to classify the outcomes as: (i) algorithm compliant, (ii) potentially algorithm non-compliant, or (iii) algorithm non-compliant. Additionally, the method includes generating controllership persona based actionable triggers, which on acceptance corrects the anomalies in financial data, enabling the method and system to perform as a comprehensive, contextual, consistent & machine learning powered, persona based real time controllership engine leading to reduced misstatements in reported financial data.

Abstract: A method and system are provided to infer and use machine learning algorithms that is applied to financial data and also to determine: (i) compliance with the applied algorithms, and (ii) whether any exceptions to and/or variations from the algorithms are within an organization's norms. A method includes: receiving financial data; applying a set of dynamic or interdependent algorithms to the received financial data to obtain a set of outcomes; and using a machine learning classifier to classify the outcomes as: (i) algorithm compliant, (ii) potentially algorithm non-compliant, or (iii) algorithm non-compliant. Additionally, the method includes generating controllership persona based actionable triggers, which on acceptance corrects the anomalies in financial data, enabling the method and system to perform as a comprehensive, contextual, consistent & machine learning powered, persona based real time controllership engine leading to reduced misstatements in reported financial data.

Abstract: The present disclosure relates to determining automation potential of a process by dividing the process into subsidiary processes corresponding to five hierarchical tiers, assessing the subsidiary processes by selecting a value for each of one or more parameters for each of the subsidiary processes at the tier five, computing a score for the subsidiary processes at the tier five based on the selected values, identifying a category of automation for the subsidiary processes in the tier five based on the score for the subsidiary processes at the tier five; determining an automation potential indicator for the subsidiary processes in the tier five based on the category of automation and the score; computing a maximum automation potential indicator and an automation potential indicator at each hierarchical tier based on a maximum automation potential indicator and an automation potential indicator at immediately lower tier to compute the automation potential for the process.

Abstract: The present disclosure relates to determining automation potential of a process by dividing the process into subsidiary processes corresponding to five hierarchical tiers, assessing the subsidiary processes by selecting a value for each of one or more parameters for each of the subsidiary processes at the tier five, computing a score for the subsidiary processes at the tier five based on the selected values, identifying a category of automation for the subsidiary processes in the tier five based on the score for the subsidiary processes at the tier five; determining an automation potential indicator for the subsidiary processes in the tier five based on the category of automation and the score; computing a maximum automation potential indicator and an automation potential indicator at each hierarchical tier based on a maximum automation potential indicator and an automation potential indicator at immediately lower tier to compute the automation potential for the process.