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Alex Korbonits is a Data Scientist at Remitly, Inc., where he works extensively on feature extraction and putting machine learning models into production. Outside of work, he loves Kaggle competitions, is diving deep into topological data analysis, and is exploring machine learning on GPUs. Alex is a graduate of the University of Chicago with degrees in Mathematics and Economics.
Abstract summary
Applications of machine learning and ensemble methods to risk rule optimization:
At Remitly, risk management involves a combination of manually created and curated risk rules as well as blackbox inputs from machine learning models. Currently, domain experts manage risk rules in production using logical conjunctions of statements about input features. In order to scale this process, we’ve developed a tool and framework for risk rule optimization that generates risk rules from data and optimizes rule sets by ensembling rules from multiple models according to a particular objective function. In this talk, I will describe how we currently manage risk rules, how we learn rules from data, how we determine optimal rule sets, and the importance of smart input features extracted from complex machine learning models.
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