Use cases for Real-time Analytics with ClickHouse in Modern Banking

Introduction

The modern banking industry faces significant challenges related to fraud prevention. Fraudsters are becoming increasingly sophisticated, and traditional fraud detection methods are often insufficient to detect and prevent fraudulent activities. Real-time fraud analytics is an essential tool for banks to detect and prevent fraud in real-time. ClickHouse is an ideal platform for real-time fraud analytics due to its ability to handle high-volume data processing, perform sub-second queries, and scale horizontally across multiple nodes.

ClickHouse for real-time fraud analytics

Let’s take an example application on ClickHouse for real-time fraud analytics in modern banking. Suppose a bank has an online banking application that allows customers to transfer funds between accounts. The bank wants to detect fraudulent activities, such as suspicious fund transfers, in real-time. Here’s how ClickHouse can be used to achieve this:

  1. Real-time data ingestion: The bank can ingest transaction data in real-time from various sources, including the online banking application, ATM machines, and other systems. ClickHouse’s high-speed data ingestion capabilities allow the bank to process this data quickly and efficiently.
  2. High-speed analytics: ClickHouse’s columnar architecture allows the bank to perform aggregations and filters on large datasets with sub-second response times, enabling the bank to analyze its data in real-time. The bank can use ClickHouse to identify suspicious fund transfers, such as transfers to unfamiliar accounts or large transfers that exceed the customer’s typical transaction history.
  3. Distributed processing: ClickHouse’s distributed processing capabilities allow the bank to scale its analytics infrastructure horizontally across multiple nodes. This enables the bank to handle large volumes of data and queries in real-time, providing high availability and scalability.
  4. Custom data processing: The bank can customize ClickHouse to suit its specific data processing needs. For example, they can create custom functions and optimized queries for their specific use cases, allowing them to extract maximum value from their data. This customized data processing allows the bank to gain insights that are not possible with off-the-shelf solutions.
  5. Real-time decision-making: The bank can use ClickHouse’s real-time data processing capabilities to make real-time decisions about fund transfers. For example, ClickHouse can be used to flag suspicious fund transfers and block them in real-time, preventing fraudulent activities from occurring.

Conclusion

In summary, ClickHouse is an ideal platform for real-time fraud analytics in modern banking. ClickHouse’s ability to handle high-volume data processing, perform sub-second queries, and scale horizontally across multiple nodes enables banks to detect and prevent fraudulent activities in real-time. The bank can use ClickHouse to ingest transaction data in real-time, perform high-speed analytics, customize data processing, and make real-time decisions about fund transfers.

To know more about ClickHouse in Banking, do consider reading the following articles:

About Shiv Iyer 222 Articles
Open Source Database Systems Engineer with a deep understanding of Optimizer Internals, Performance Engineering, Scalability and Data SRE. Shiv currently is the Founder, Investor, Board Member and CEO of multiple Database Systems Infrastructure Operations companies in the Transaction Processing Computing and ColumnStores ecosystem. He is also a frequent speaker in open source software conferences globally.