Machine learning is becoming an essential technology, no matter the industry. Data is now the heart and soul of business, and comprehending it effectively is crucial to making decisions. For that reason, financial executives need to insert themselves into the conversation about the right data to use, applying their institutional knowledge while simultaneously understanding key principles of machine learning.
The aim of this webinar is to demystify the use of ML and give finance professionals a way to decide how to use it, understand how it works, and assess outputs and the data science to make it successful.
The seminar will look at:
Download Alvarez & Marsal's recent report here: OPENING THE BLACK BOX: HOW TO ASSESS MACHINE LEARNING MODELS
Chandu Chilakapati is a Managing Director with Alvarez & Marsal Valuation Services in Houston. He specializes in fair value and financial reporting of financial instruments, as well as traditional business and asset valuation advisory services; he is also the practice’s Head of Innovation. He is one of the founders of LeaseSCRE, a machine-learning software that estimates a company's incremental borrowing rate (IBR).
Mr. Chilakapati started ValSource, Alvarez & Marsal’s web-based software that delivers valuation outsourcing efficiently on a range of complex financial instruments. He has also created numerous valuation models that include Monte Carlo simulation for path-dependent instruments such as separable warrants, convertible debt and stock-based compensation.
He earned a bachelor’s degree in economics from Washington University in St. Louis, and an MBA from Rice University. He has spoken at and moderated conferences related to technical accounting, machine learning, and other innovation related topics.
Chandu has recently co-authored an Alvarez & Marsal guide for finance executives, together with Devin Rochford, on the various considerations that finance professionals need to keep top-of-mind for their organizations' strategic data initiatives. 'Opening The Black Box: How To Assess Machine Learning Models'
Tuesday, 20 October 2020
16:00 - 16:45 London Time