Fintech Series Part 3: What New Skills Are Needed for Finance?

Welcome to my series on fintech’s evolving role in finance! Part one can be found here, and part two can be found here.

We’ve thus far examined how fintech is transforming the financial industry, so what does this transformation mean for would-be quants; what new skills are needed for finance? In this blog post, I’ll introduce the new “full-stack” quant and discuss whether the quant is becoming a data scientist. Is fintech killing the “traditional” banking job?

Fintech and tech are expanding: the industry seems to look for the “full-stack” quant, someone who is good at coding, but who also has financial intuition and excels at statistics. This new quant is a coding polyglot, able to use Python and R for fast implementation; C++, Java, and JavaScript for backend-support; SQL and NoSQL (like MongoDB) for database familiarity. In addition to mastering multiple programming languages, the quant also possesses market intuition and a strong grasp on pricing theory. Furthermore, a firm understanding of statistics is critical. Computational statistics and machine learning (especially data-driven and AI-related models) are the hottest topics today. Years ago, at investment banks like Goldman Sachs, one needed to be very good at pricing. Nowadays, everyone needs some level of machine learning and artificial intelligence competency.

“Years ago, at investment banks like Goldman Sachs, one needed to be very good at pricing. Nowadays, everyone needs some level of machine learning and artificial intelligence competency.”

The typical finance student understands stochastic calculus modeling for model risk management, probability theory for VaR calculations, basic stats such as regression analysis, and basic coding (R, SAS, Matlab) for implementation and stress testing. The full-stack quant is much stronger in coding and statistics. Top talent at fintech firms understand econometrics modeling, such as time-series and its variations, machine learning, such as random forest and gradient boosting (Kaggle-level modeling), coding (Python/R) for production-level codes, and database management (SQL/NoSQL) for data querying/pre-processing. Product intuition is good for career advancement; strong communication and people skills are essential for collaboration across teams; and text analytics, the process of deriving gainful information from text through text categorization, text clustering and sentiment analysis, is an increasingly useful skill to have.

Below is a chart stressing the distinctive skills needed at an investment bank versus a fintech firm:

As you can see, fintech firms are much more data-centric and, if developments in fintech signal the future of finance, the full-stack quant will become the norm rather than the exception. So, is the quant becoming a data scientist?

There are fewer opportunities in “traditional” finance, though there are still jobs available, such as in Sales and Trading (S&T) - these positions are now just a lot more technical. Profit margins on new products are so small that, to compensate, banks are selling solutions to clients (e.g. investment strategies to insurance companies). But the finance industry does not need “just programmers.” There’s a strong mutual interest between our students and companies like Facebook, Google, Uber and Unison. Students themselves want to work in fintech - it’s a more challenging field with a “better culture” according to them. The quant isn’t becoming a data scientist, per se, but becoming “full-stack”: combining programming, statistical analysis and financial intuition. So will fintech kill your banking job? No!

“Will fintech kill your banking job? No!”

The industry has undergone a number of changes, including a saturation of quant positions on the sell-side, difficulty in placing students with weak programming backgrounds on the buy-side, and the rise of fintech companies and fintech activities among investment banks. To stay on the cutting edge, traditional quant finance curricula needs to adapt by increasing pre-requisites in programming (viz. Python and R) and adding more data-relevant courses, such as in machine learning. In my next post I’ll discuss how the Berkeley MFE Program is rising to the call.

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Photo credits: Monito - Money Transfer Comparison via VisualHunt /  CC BY