在反洗钱（AML）领域，传统的基于规则的方法正在被图机器学习的变革所取代。由于金融交易量不断增加和误报警报普遍存在，人们更倾向于开始采用创新技术来提高调查效率。图机器学习通过解析金融数据中实体之间的复杂关系，成为解决方案的关键。
风控系列研讨会共包含三期，分别围绕团伙反欺诈、反洗钱、风险识别和信用评估。每期都会结合实际业务场景，由浅入深，并利用Graph Studio可视化分析工具为大家在线demo。这里有一些方法可以帮助你构建更好的风险管理解决方案。
Anna Veronika Dorogus
Machine Learning Expert
Anna Veronika Dorogush graduated from Lomonosov Moscow State University and Yandex School of Data Analysis. She used to work at ABBYY, Yandex, Microsoft and Google on Machine Learning infrastructure and Machine Learning frameworks. In 2017 she published the open-source library CatBoost, which is now one of top-3 most popular Gradient Boosting libraries, and the top 7-th most used Machine Learning framework in the world according to Kaggle 2021 review.
