Beyond Big Data Gathering: Creating Value from Feature Engineering

Gone are the days where gathering and organizing vast lakes of data was enough to create value for organizations. Financial institutions in particular have captured significant amounts of data for years but are failing to truly use this asset in ways that deliver value for their organizations and customers.

Looking Past the Algorithm

Best Practices for Feature Engineering

  • Calculate “Estimated Value” for a home using an average of “Comparable Sales” by “Square Footage”
  • Produce DTI by calculating ratio of “Credit Payments” to “Current Income”
  • Derive a “Retirement Gap” by calculating the “Future Value” of existing assets and comparing to “Current Income”
  • Scaling values between min-max of a variable such as age in the dataset into a range of [0, 1]
  • Examining the number of purchases in particular types of retail stores as an indicator of “interest” in certain consumer goods
  • Principal Component Analysis (PCA) and Independent Component Analysis (ICA) map existing data to another feature space
  • Deep Feature Synthesis (DFS) allows for transfer of intermediate learnings from middle layers in the Neural Networks

Key Steps for Success


Thanks for reading. We hope you enjoyed another contributed article by hands-on industry experts. Let them know you liked it by clicking the 👏 button — and holding it down.

Visit us on Twitter and don’t miss the current fintech newsletter issue here.



Insights into where finance meets technology - from experts, for experts.

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store
FinTech Weekly

FinTech Weekly is a news service for the FS industry. Our newsletter comes out weekly, wrapping up the most important insights and strategies from the past week