Applications of machine learning and ensemble methods to risk rule optimization

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 black-box 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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