Uplift Modeling

By Alejandro Correa and Maria Fernanda Cortes  This post is part of a series in which I’m discussing several parts of my AI_at_Rappi presentation. In a previous post I discussed a particular algorithm for recommending restaurants called rest2vec, In a follow-up, I discussed how to include financial costs when analyzing a churn model.  This time... Continue Reading →

Building AI Applications Using Deep Learning

Recently, we have seen a huge boom around the field of deep learning; it is currently being implemented in a wide variety of fields, from driverless cars to product recommendation. In their most primitive form, deep learning algorithms originated in the 1960s. If the concept has been around for decades, why is it that widespread... Continue Reading →

TDWI: 5 Minutes with a Data Scientist: Alejandro Correa Bahnsen of Easy Solutions Lead data scientist Alejandro Correa Bahnsen develops machine learning algorithms for fraud detection. He described for Upside the basic skills and personality traits he believes are necessary to succeed in data science. [Read More]

Applying Data Science to Fraud Prevention

Eighty thousand Kindle users. Sixty-five million Tumblr users. What do they have in common? Both groups had their login credentials breached, courtesy of hackers. While these attacks didn’t directly target financial accounts,the information contained in these breaches is likely being sold on the Dark Web and being used to build a larger profile that will... Continue Reading →

Feature Engineering for Fraud Detection Models

As cybercriminals are constantly updating their strategies to avoid being detected, traditional fraud detection tools, such as expert rules, are less effective as they do not incorporate recent fraud patterns as fast as the fraudsters are changing their behavior. To incorporate the fraudulent behavior fast, it is important to use advanced machine learning algorithms, such as... Continue Reading →

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