Financial fraud prevention is a race against time. Implementation-wise, it relies heavily on the data processing power, especially under large datasets. Today, I'm going to share with you the use case of a retail bank with over 650 million individual customers. They have compared analytics components, including Apache Doris, ClickHouse, Greenplum, Cassandra, and Kylin. After five rounds of deployment and comparison based on 89 custom test cases, they settled on Apache Doris because they witnessed a six-fold writing speed and faster multi-table joins in Apache Doris as compared to the mighty ClickHouse.
I will get into details about how the bank builds its fraud risk management platform based on Apache Doris and how it performs.