DataHour: Quantum Computing Applications in Financial Industry

The financial services industry has been the early adopter of technological advances and sophisticated models such as the Black Scholes-Merton model for pricing financial instruments. With the advancement of technological challenges and the increase in data, classical computing is not able to achieve performance. Applying emerging quantum technology to financial problems is the solution of the future especially when dealing with uncertain events, constraints optimization, frauds etc. Benefits include greater compliance, employing behavioural models to enhance customer engagement and faster reaction to market volatility.

The solution medium of quantum computing is orders of large magnitude larger than traditional computers even immensely powerful ones. Hence, multiplying the power of classical computers requires about double the number of transistors engaged in solving the problem. The power of a quantum computer can be approximately doubled each time one qubit is added. This session provides an overview of applications of quantum computing for Financial Institutions.

Prerequisites: It is beneficial to have a basic familiarity with Machine Learning and Quantum Computing for this webinar.

Saurabh Pramanick is a Data Scientist and Vice President at a Leading Investment Bank, specializing in Data Analytics, Fraud Analytics, and Data Governance. With over 12 years of experience, he has led successful initiatives in implementing Data Lake, Data Science, and Data Governance, resulting in impressive fraud reduction and improved data quality. He has a strong track record in regulatory reporting and holds various certifications in Data Management and Cloud Computing. Saurabh is pursuing a Ph.D. in Computer Science Engineering.