How Orange Financial combats financial fraud over 50M transactions a day using Apache Pulsar
Who is this presentation for?
- Data scientists, software engineers, and CTOs
As a fintech company of China Telecom with half of a billion registered users and 41 million monthly active users, Orange Financial has dozens of online financial products. The company faces threats from financial fraud every day, such as identity theft, money laundry, affiliate fraud, merchant fraud, etc. Risk control is vital, and the company has thousands of decisions running against each transaction to fight against these threats in its risk management system.
Weisheng Xie and Jia Zhai explore how Orange Financial leverages Apache Pulsar to boost the efficiency of its risk-control decision development.
In the risk-management scenario, the core is decision making. Decisions are composed of a series of rules and models. Needless to say, the development of rules and models is vital, but another part that’s equally important is the manufacturing of the indicators and features required by the decisions. Some indicators of Orange Financial’s risk-management system, for example, are the intimacy between users, the monthly average consumption frequency and money, the login frequency in the last minute and the last month and year, and the time interval between the last two transfer transactions, etc. Clearly, some of these indicators require large volume of historical data stored in a data store, Hive, for example, and are computed normally in batch mode (e.g., Presto in this case); some indicators depend on data in the current transaction and are needed by decisions of current transaction; the real-time transaction data is stored in a message queue such as Kafka, streaming computation is widely adopted (e.g., Spark Streaming). This is a typical Lambda architecture and has been running for many years at Orange Financial.
The biggest detraction to this architecture has been the need to maintain two distinct (and possibly complex) systems to generate both batch and speed layers. Kappa attempts to simplify by only keeping one code base rather than manage one for each batch and speed layers in the Lambda architecture. The complication of this architecture mostly revolves around having to process this data in a stream, such as handling duplicate events, cross-referencing events, or maintaining order—operations that are generally easier to do in batch processing. Still, the company has been seeking a solution that can unify the data store, computing engine, and programing language for decision development in its risk control system.
Apache Pulsar is an open source distributed event streaming system originally created at Yahoo and now part of the Apache Software Foundation. Apache Pulsar addresses the messy operational problems by storing data in segmented streams. The data is appended to topics (a.k.a., streams) as they arrive, and segmented and stored in a scalable log storage, Apache BookKeeper. As the data is stored as only one copy (source of truth), it addressed the inconsistency problem in Lambda architecture. Also the data can be accessed in Streams via unified pub/sub messaging and segments for elastic parallel batch processing. It makes Apache Pulsar a perfect unified messaging and storage solution. Together with a unified computing engine like Spark, it can boost the efficiency of Orange Financial’s risk-control decision deployment.
- A working knowledge of big data, data processing, and pub/sub messaging
What you'll learn
- Understand Lambda architecture and Apache Pulsar
- Discover how Orange Financial uses Lambda for risk-control decision deployment and how it boosts efficiency by leveraging Pulsar
Vincent Xie (谢巍盛) is the Chief Data Scientist/Senior Director at Orange Financial, as head of the AI Lab, he built the Big Data & Artificial Intelligence team from scratch, successfully established the big data and AI infrastructure and landed tons of businesses on top, a thorough data-driven transformation strategy successfully boosts the company’s total revenue by many times. Previously, he worked at Intel for about 8 years, mainly on machine learning- and big data-related open source technologies and productions.
Jia Zhai is a founding engineer of StreamNative, as well as PMC member of both Apache Pulsar and Apache BookKeeper, and contributes to these two projects continually.
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