One of the first industries to extensively use advanced maths to do better was financial services. Ever since Fischer Black and Myron Scholes published their seminal paper on option pricing in 1973, Wall Street firms hired mathematicians and scientists by the droves, getting them to model asset prices in order to get an edge in the market. Even today, top hedge funds such as Renaissance, Citadel and Two Sigma prefer to hire scientists rather than finance professionals to manage their portfolios.

However, in the last decade or so, as Data Science and Artificial Intelligence have taken over the rest of the world, Wall Street has not maintained its leadership position in the use of maths to make money. How and why did this happen?

In order to understand this, we talk to Hari Balaji, co-founder of Romulus, an award winning unstructured data automation platform for Financial Services firms.

Prior to founding Romulus, Hari spent a decade in quant & data roles at Goldman Sachs across Hong Kong and Singapore. Hari is an alumnus of IIT Madras & IIM Ahmedabad.

Show Notes

00:03:15 - What is data science and what is artificial intelligence?

00:10:40 - What Hari’s company does

00:14:00 - Toolbox versus hammer-nail approaches

00:15:00 - This history of math in the financial services industry

00:28:45 - Wall Street is never a first mover but a great follower

00:33:30 - How Wall Street uses data science nowadays

00:41:00 - Why most innovations have happened at smaller firms

00:44:00 - Why the financial industry doesn’t behave like the Tech world

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Data Chatter is a podcast on all things data. It is a series of conversations with experts and industry leaders in data, and each week we aim to unpack a different compartment of the "data suitcase".