Apache Kafka Use Cases in Banking

Updated: 9 hours ago



In the era of digitalization, real-time streaming data is utmost important to all digital platforms. This article shares several use cases in banking for real-time streaming data transmission and analysis by Apache Kafka.

Apache Kafka is a distributed and high throughput message broker for streaming data transmission over the network. It supports publish and subscribe topology and provides functions of message storing, distribution and analysis.

Why Apache Kafka is fast?

The high performance of Kafka is due to its specific design for streaming data processing, such as Highly distributed architecture for horizontal scaling, so that stream data could be partitioned into different parts and sent through several servers across the network. Hence data transmission is low latency, high throughput, and fault tolerant.



In addition, the message payload of Kafka is a simple key-value pair which gives high efficiency in payload operations for unbound and continuous streaming data. To minimize the network transmission overhead, the streaming data is batched and compressed before it is sent over the network which gives a significant performance gain against each data being sent individually.

Apache Kafka Use Cases in Banking

Given the powerful functions for streaming data transmission, there are several use cases of Kafka in banking. These use cases are difficult to be implemented with traditional message brokers like ActiveMQ or RabbitMQ.

  • Real-Time Applications Status Dashboard – various applications can send the execution or error log, and health status as a real-time data stream to Kafka which can store the data for dashboard presentation and analytics.


  • Real-Time Market Data Streaming – this is a typical use case of Kafka. Market data such as stock price could be a Kafka topic producer. Those subscribers of this Kafka topic could receive the market data as a continuous data stream.


  • Real-Time Transaction Analysis – In a high-volume trading environment, thousands of transaction events happen in every second. These events could be serialized and sent to Kafka. They will be saved in chronological sequence and ground for various real-time market metrics.


  • Real-Time Risk Analysis – In the risk management domain, time to respond is always the most imperative factor for efficient risk management. Traditional risk management over the trading operation may have a time lag between risk appearing and response. By analysing transaction events and market data, we can identify and manage various trading risks, such as fraud or credit risk, in real-time.


  • Machine Learning – In machine learning, training data is the most crucial thing to the accuracy of the artificial intelligence system. Stream data provides vast amounts of data for training purposes. That makes Kafka the best tool to support machine learning.


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