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Banks were among the first businesses to use computing technology to streamline their business. Bank of America pioneered electronic check processing with ERMA (Electronic Recording Machine, Accounting) in the 1950s. Fast-forward to today where banks are using cloud and mobile technology to service customers all over the world. So if banks and the finance industry are not new to technology then what do we really mean by fintech, the new new thing?

The first commercial computers were built for large corporate customers by the likes of IBM, Honeywell, NCR, and RCA. Data was seldom moved between one system and another and all tools and programs were written specifically for each. Today we build systems by assembling services and components from a variety of sources. Developers, architects, and product managers focus most attention on the part of their system that differentiates them from the competition or gains them a competitive advantage. This acceleration is super-charging digital transformation in almost every industry and it is specifically this effect in the banking and finance industries that we call fintech. And the navigators on this journey are called data scientists.

Data scientists usually have domain expertise. In finance, they would understand the mechanisms of asset pricing, portfolio management, credit risk scoring, or money laundering prevention, for example. But what sets them apart from traders, risk analysts, and other stalwarts of quantitative finance, is their technology toolbox. Data scientists help bridge the gap that typically exists between engineering and business. They work closely with engineers to make technology choices that facilitate delivery of business value: database platforms supporting volume, velocity, and variety of data; modeling platforms compatible with the problem domain; analytics to describe and predict operating conditions. From the business side, data scientists will identify which capabilities can be rented versus those that must be built internally, as well as help keep one foot firmly on the ground in terms of technical feasibility. Data scientists will spearhead innovation with exploratory analysis that quickly identifies dead-ends and re-focuses the business, as well as validating new initiatives before committing resources.

So from whence will this data scientist clone army spring you might ask. The answer is that they already live among us: the software engineer using regression analysis for capacity planning, the derivatives trader using sci kit-learn to price assets, and the database administrator using PostGIS for geospatial analysis.