Data Science Vs Financial Engineering

A lot of aspiring financial professionals or even those who are in the financial industry but looking at new vistas in finance ponder if they should specialize in data science or financial engineering. Perhaps three to five years ago they could be treated as completely different skill sets. Increasingly though as data science matures, niche areas are developing. Financial data science is fast emerging as one of the fastest growing careers. Financial engineers have a skill set that are both difficult and lucrative. What’s not to want in a person who has not one but two sets of deep analytical skills? Let’s shed some light on the data science vs financial engineering debate.

Instead of jumping to conclusions vying for one over the other, it would be a good idea to assess both areas independently.

Financial Engineering

The Norman and Adele Barron Professor of Management at Boston University, Zvi Bodie, defined Financial Engineering as:

“the application of science-based mathematical models to decisions about saving, investing, borrowing, lending, and  managing risk”

Financial engineering is also known as computational finance or mathematical finance. Some of the organizations that employ financial engineers include regulatory agencies, commercial banks, hedge funds, insurance companies and corporate treasuries.

Financial engineering is applied to many areas in finance such as pricing derivatives instruments, financial regulations, deal execution, corporate finance, portfolio management, risk management, trading and structured finance. There is a lot of focus on assessing and managing risks in financial products.

In its true sense, a financial engineer is a specialist who works with mathematical formulas and programming tools, and applies his knowledge to areas of finance to build data-driven financial models. How a financial engineer adds value is by helping in improving the quality of financial products as well as create new financial products.

A financial engineer is also expected to work as a node between finance professionals and the tech team. They help provide development skills for analytical processes. They also help develop financial and analytical strategies for decision-making. They have to keep a watch for new and upcoming trends with regards to fiscal processes, big data and the like.