Detecting Fraud using Machine Learning Models | Jesse Barbour | Stories in AI

In this episose of the Stories in AI podcast, I sit down with Jesse Barbour, Chief Data Scientist of Q2ebanking. At their company, Jesse and the team are working to revolutionize the way financial institutions interact and transact with their customers. With this, Jesse provides valuable insight to what it takes to build effect machine learning models to detect fraud and bias. Hope you enjoy it!

Jesse's Bio:

Jesse Barbour is the Chief Data Scientist at Q2ebanking. He uses machine learning to discover hidden structure, insight, and value from data. His work has paved the way for multiple ground-breaking products including Sentinel, Q2’s flagship security offering, and SMART, the company’s targeting and messaging platform. Jesse’s 14 years in the financial services industry have been driven by a relentless passion to deliver technologies that have a fundamental, positive impact on individuals and communities.

LinkedIn: https://www.linkedin.com/in/jbarbour/

 

A note about our sponsor: Experian is the world’s leading global information services company. We empower our clients to manage their data with confidence and build trusted relationships with consumers, using advanced analytics, decisioning technology, and fraud prevention tools. We help businesses to make smarter decisions and thrive, lend more responsibly, and prevent fraud and financial crime.

As the world’s leading repository of consumer credit data, Experian is transforming data into solutions that facilitate transactions, ensure financial safety and improve the financial lives of millions of consumers around the world.