Digital Transformation in Modern Banking with ClickHouse-powered Real-time Analytics

Introduction

Internal and external frauds can devastate the digital banking business, both in terms of financial losses and damage to the institution’s reputation. Here are some ways in which internal and external frauds can destroy digital banking businesses:

  1. Financial Losses: Internal and external frauds can result in significant financial losses for digital banking businesses. Internal frauds are committed by employees or insiders with access to sensitive information, while external frauds are committed by individuals or groups outside the organization. Both types of fraud can result in losses due to stolen funds, unauthorised transactions, or other types of financial fraud.
  2. Damage to Reputation: Fraudulent activities can damage the reputation of digital banking businesses, resulting in a loss of customer trust and loyalty. Customers may be reluctant to do business with an institution that has experienced fraud, and negative publicity can damage the business’s brand image.
  3. Legal and Regulatory Penalties: Digital banking businesses that experience fraud can face significant legal and regulatory penalties, including fines and sanctions. Failure to comply with regulatory requirements can result in the revocation of the institution’s license to operate, leading to a complete business shutdown.
  4. Operational Disruption: Fraudulent activities can disrupt the operations of digital banking businesses, leading to downtime, delays, and reduced efficiency. Fraudulent activities can also result in additional workload and expenses, as businesses are forced to investigate, resolve, and prevent further fraudulent activities.
  5. Cybersecurity Threats: Internal and external frauds can also pose a significant cybersecurity threat to digital banking businesses. Fraudsters may use social engineering tactics, phishing attacks, or other methods to access sensitive information, which can then be used for further fraudulent activities.

Most common internal and external frauds happening in the modern digital banking business

  1. Phishing: Phishing is a type of social engineering attack where fraudsters send fraudulent emails or messages to customers, posing as a legitimate financial institution, and request personal or account information. This information can then be used to perform fraudulent activities.
  2. Account Takeover: Account takeover occurs when fraudsters gain unauthorized access to a customer’s account by stealing their login credentials, usually through a phishing attack. Once they can access the account, they can perform unauthorized transactions or steal funds.
  3. Malware: Malware is malicious software that is designed to infect computer systems and steal sensitive information. Fraudsters can use malware to steal login credentials, account information, or other sensitive data.
  4. Insider Fraud: Insider fraud occurs when employees or other insiders of a financial institution use their access to systems or information to commit fraud. Insider fraud can include theft of funds, unauthorized access to customer information, or other types of fraudulent activities.
  5. Card Skimming: Card skimming involves using devices to steal credit or debit card information, usually from ATMs or payment terminals. Fraudsters can then use this information to make fraudulent purchases or withdraw funds.
  6. Money Mule: Money mules are individuals who are recruited by fraudsters to receive and transfer funds as part of a fraudulent scheme. Fraudsters use money mules to launder money or transfer funds to offshore accounts to avoid detection.
  7. Social Engineering: Social engineering involves using psychological manipulation to trick individuals into providing sensitive information or performing unauthorized actions. Fraudsters can use social engineering tactics to access systems or steal sensitive information.

Digital Transformation in Banking involves migration from traditional OLAP Stores to ClickHouse for modern real-time Analytics.

Traditional OLAP (Online Analytical Processing) systems are designed to handle complex queries and large amounts of data in a batch processing mode. These systems work well for historical analysis and reporting, where the data is aggregated and analyzed at regular intervals. However, they are not well-suited for real-time analytics, where data is analyzed as it arrives.

In contrast, ClickHouse is a real-time analytics system that is designed to handle large amounts of data with low latency. It uses a columnar storage format that allows for the efficient processing of complex queries, and it is optimized for OLAP workloads that require high throughput and low latency.

In the context of fraud detection systems in financial institutions, real-time analytics systems like ClickHouse are essential for several reasons:

  1. Timely Detection of Fraudulent Activities: Financial institutions need to be able to detect fraudulent activities as soon as possible to minimize their losses and protect their customers. Real-time analytics systems like ClickHouse enable them to analyze transaction data as it arrives and identify potential fraudulent activities in real time.
  2. Complex Querying: Fraud detection systems often require complex queries that involve multiple tables and data sources. Traditional OLAP systems can struggle with these types of queries due to their batch-processing nature. ClickHouse, on the other hand, is optimized for these types of workloads and can handle complex queries with low latency.
  3. High Volume of Data: Financial institutions generate and process massive transactional data. Real-time analytics systems like ClickHouse can handle this volume of data with low latency, which is essential for responsive fraud detection and control systems.
  4. Real-time Decision Making: Fraud detection systems require real-time decision-making capabilities to prevent fraud as it happens. Real-time analytics systems like ClickHouse can provide this capability, allowing financial institutions to take immediate action in response to potentially fraudulent activities.

In conclusion, traditional OLAP systems are not well-suited for real-time analytics workloads like fraud detection systems in financial institutions. Real-time analytics systems like ClickHouse are essential for modern complex financial services systems for responsive fraud detection and control systems. They enable financial institutions to analyze data as it arrives, handle complex queries with low latency, and provide real-time decision-making capabilities, which are critical for detecting and preventing fraud in real time.

Why do we recommend ClickHouse over many other columnar database systems?

  • Compact data storage – Ten billion UInt8-type values should exactly consume 10GB uncompressed to efficiently use the available CPU. Optimal storage even when uncompressed benefits performance and resource management. ClickHouse is built is store data efficiently without any garbage.
  • CPU efficient – Whenever possible, ClickHouse operations are dispatched on arrays, rather than on individual values. This is called “vectorized query execution,” and it helps lower the cost of actual data processing.
  • Data compression – ClickHouse supports two kinds of compression LZ4 and ZSTD. LZ4 is faster than ZSTD but the compression ratio is smaller.ZSTD is faster and compresses better than traditional Zlib but slower than LZ4.  We recommend customers LZ4 when I/O is fast enough so decompression speed will become a bottleneck. When using super ultra-fast disk subsystems you have an option to specify “none” compression. ZSTD is recommended when I/O is the bottleneck in queries with large range scans.
  • Can store data in disk – The columnar database systems like SAP HANA and Google PowerDrill can only work in the RAM.
  • Massively Parallel Processing – ClickHouse is capable of Massively Parallel Processing very large/complex SQL(s) optimally and cost-efficiently
  • Built for web-scale data analytics – ClickHouse supports sharding and distributed processing, This makes ClickHouse the most preferred columnar database system for web-scale. Each shard in ClickHouse can be a group of replicas addressing maximum reliability and fault tolerance.
  • ClickHouse support Primary Key – ClickHouse permits real-time data updates with a primary key (there will be no locking when adding data). Data is sorted incrementally using the merge tree to perform queries on the range of primary key values.
  • Built for statistical analysis and supporting partial aggregation – ClickHouse is a statistical query analysis-ready columnar database store supporting aggregate functions for approximated calculation of the number of various values, medians, and quantiles. ClickHouse supports aggregation for a limited number of random keys, instead of for all the keys. You can query on a part (sample) of data and generate approximate results reducing disk I/O operations considerably.
  • Supports SQL – ClickHouse supports SQL, Subqueries are supported in FROM, IN, and JOIN clauses, as well as scalar subqueries. Dependent subqueries are not supported.
  • Supports data replication – ClickHouse supports asynchronous multi-master and master-slave replication.

Why do successful companies work with ChistDATA for 24*7 ClickHouse Consultative Support and Managed Services?

  • ChistaDATA provides full-stack ClickHouse Optimization. We deliver elite-class Consultative Support (24*7) and Managed Services for both on-premises ClickHouse infrastructure and Serverless/Cloud/ClickHouse DBaaS operations.
  • ChistaDATA Server for ClickHouse (and all tools essential for Data Ops. @ Scale) will be Open Source (100% GPL forever) and free. We are committed to helping corporations in building Open Source ColumnStore for high-performance Data Analytics.
  • Global Team available 24*7 for ClickHouse Consultative Support and Managed Services.
  • Our team has built and managed Data Ops. Infrastructure of some of the largest internet properties. We know very well the best practices for building optimal, scalable, highly reliable and secured Database Infrastructure @ scale.
  • Lean Team Culture: Startup-friendly and specialists in DevOps. and Automation for Database Systems Maintenance Operations.
  • Transparent pricing and no hidden charges – We have both fixed-priced and flexible subscription plans.
  • Based out of San Francisco Bay Area. But, we have global teams operating from 11 cities worldwide to deliver 24*7 Consultative Support and Managed Services for ClickHouse.

Conclusion

Digital banking faces significant fraud risks, countered by real-time analytics with ClickHouse. It enables timely fraud detection, handles complex queries efficiently, and supports high data volumes. ChistaDATA offers expert ClickHouse support for optimal banking operations.

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About Shiv Iyer 215 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.