Leveraging graph-based features to overcome the accuracy barrier in fraud detection machine learning
Despite spending $1Bn on fraud detection software each year, fraud still costs banks over $50Bn annually, and it’s not going down. Many data scientists are building and tuning machine learning models, but fraud detection rates aren’t improving much. The problem is not the algorithms; it’s the data you put into them. US tier 1 banks report Fraud Detection ML breakthroughs by introducing graph features into their existing fraud detection models. Graph features add brand-new information to your Machine Learning model, leading to a quantum leap in accuracy.