Tata Consultancy Services Limited

Divya Vasudevan

Devraj Goulikar

Ashish Omprakash Indani

Kamlesh Mhashilkar

Saurabh Das

Abhay Sodani

Aparna Joshi

Rajdeep Sarkar

Vamsee Krishna Kodali

Prita Venkateswaran

Ashutosh Pachisia

Prashant Chaturvedi

Rohit Kadam

Vimal Chaubey

A method and system for automated continuous validation for regulatory compliance of CS with dynamic component. On identification of learning in the CS, a User Acceptance Testing (UAT) is performed using automated test cases of varying types in accordance with what-if scenarios and synthetic data generated using a unique approach. Thereafter, a base validation testing of the CS is performed with clean data (positive scenarios of outcome of the CS) and dirty data (negative scenarios) by conducting repeatability, stability (consistency) and reliability checks. The base validation testing is then followed by learning saturation testing on only if the dynamic component is validated, is rolled out in production environment else is rolled back to the earlier version.

Automated continuous validation for regulatory compliance of a computer system (CS) comprising a dynamic component

With the scale of information available today along with the existing diverse channels of communication, manual processing of information is becoming a challenge and companies across industries are under tremendous pressure to lower transactional costs. Artificial Intelligence based automation of business transactions has seen regulatory hurdles due to probabilistic nature of the outcome. The main challenge lies in processing of transactions with unstructured information. Systems and methods of the present disclosure uses deterministic as well as probabilistic approaches to maximize accuracy. The larger use of deterministic approach with configurable components and ontologies helps to improvise accuracy, precision and reduce recall. The probabilistic approach is used when there is absence of quality information or less information for learning. Also, confidence indicators are provided at attribute level of data being processed and at each decision level.

Automated cognitive processing of source agnostic data

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Pune, India

Divya Vasudevan