Use Cases›Fraud Detection

Real-Time Fraud Detection

As our digital presence grows more interconnected and exploitable, fraud remains a significant business challenge. Hazelcast offers real-time fraud detection solutions that instantly identify fraud to prevent financial losses.

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Introduction

Fraud detection systems are essential for uncovering hidden patterns and relationships within a company's ecosystem, combating various financial crimes like payment fraud, credit card fraud, and money laundering. Neglecting system upgrades can pose serious risks to customers, investors, and a company's financial stability. Leveraging Hazelcast, a major credit card provider reduced annual fraud write-downs by $100 million amidst rising transaction volumes and sophisticated hacking attempts.

Announcing Fraud Detection advancements in Hazelcast Platform 5.5

Enhanced fraud detection for major financial institutions, processing millions of payment transactions a second, enriching data in-flight, and identifying fraudulent activity with similarity search for improved accuracy in flagging potential fraud. 

Business Requirements

Over the years, the demands for fraud detection have become increasingly stringent. Therefore, staying updated on the latest trends and requirements is crucial to effectively combat fraud now and in the future. Current common business requirements for fraud detection include:

  • Ongoing effort to improve fraud detection accuracy, to minimize fraud loss as well as to minimize false positives, all while keeping deployment costs under control
  • Deployment of more sophisticated fraud detection algorithms, going beyond a rules-based approach and leveraging machine learning strategies
  • Simplifying the deployment of machine learning models so that improved algorithms can be leveraged as soon as possible
  • Seamless update of fraud detection algorithms to avoid downtime
  • Ensure predictable throughput and latency, even during traffic spikes, to meet stringent service-level agreements (SLAs)
  • High throughput and low latency to support growing transaction loads without having to continually add hardware
  • Comprehensive security controls to protect personally identifiable information (PII)

Technical Challenges

Due to the high expectations for fraud detection and prevention, there are many technical challenges that can be potential hurdles, such as:

  • Difficulty in getting machine learning-based fraud detection models into production
  • Updating fraud detection models in production on a continual basis without service disruption
  • Minimizing the overhead of a complex system to stay within budget constraints
  • Maintaining a 24/7 operation with zero downtime
  • Maintaining predictable throughput and latency even during load spikes

Why Hazelcast

Hazelcast Platform excels in fraud detection deployments, showcasing superior performance and reliability. It has a proven track record in preventing fraud, with one card-issuing bank saving millions of dollars of fraud avoidance each year with Hazelcast Platform at the core of the real-time fraud prevention system. With a consolidated architecture, our platform streamlines the fraud detection lifecycle, reducing complexity. Partnering with Hazelcast for your fraud detection offers these key advantages:

Easy to Develop and Deploy

Hazelcast Platform was designed to simplify the application development process by providing a familiar API that abstracts away the complexity of running a distributed application across multiple nodes in a cluster. This allows developers to spend more time on business logic and not on writing custom integration and orchestration code. Our platform can seamlessly integrate with your IT architecture to add new capabilities without having to rip and replace your existing stack. The Hazelcast cloud-native architecture requires no special coding expertise to get the elasticity to scale up or down to meet highly fluctuating workload demands.

Performance at Scale

Whether you process a large volume of transactions, enhance online experiences with faster responsiveness, run large-scale transformations on data, or cut costs with a mainframe integration deployment, Hazelcast Platform is designed for the ultra-performance that today’s banking workloads require. The proven performance advantage is especially valuable for data-focused experimentation that enables ongoing business optimization, especially in data science initiatives including machine learning inference for fraud detection.

Mission-Critical Reliability

With built-in redundancy to protect against node failures, and efficient WAN Replication to support disaster recovery strategies that safeguard against total site failures, Hazelcast Platform was built to provide the resilience to run mission-critical systems. The extensive built-in security framework protects data from unauthorized viewers, and security APIs allow custom security controls for sensitive environments.

Customer Success Story

A prominent U.S. bank faced transaction rate limitations while attempting to apply fraud detection algorithms to customer data on an aging relational database platform. This technical constraint led to the breach of fraud detection SLAs, becoming a hindrance to new business initiatives.

Hazelcast was employed to store and manage 2TB of customer data, projected to expand to 5TB. This in-memory storage solution enabled the bank to effortlessly process 5,000 transactions per second, with future expectations to handle 10,000 transactions. Additionally, WAN replication ensured operational continuity by synchronizing multiple Hazelcast clusters in case of regional data center failures.

The performance benefits of Hazelcast Platform improved fraud detection accuracy, subsequently reducing fraud losses. Hazelcast offered the required performance capacity to execute multiple fraud detection algorithms concurrently within the transaction processing pipeline. Each algorithm evaluated transactions using different factor combinations and weightings. These added layers of fraud detection enabled the computation of a composite score that surpassed the accuracy of individual algorithms. This not only curtailed financial loss from fraud but also minimized "false positives," retaining transaction fees from valid purchases.

Utilizing Hazelcast Platform as a core element, the bank annually saves approximately $100 million by preventing fraud losses through an enhanced and precise fraud detection system.

Use Cases

Fraud detection is especially critical for financial transactions, but can also be valuable in preventing other types of illegitimate activities that cause bigger problems downstream, like identity theft. Example use cases for fraud detection include:

  • Credit card payments, including both in-person and online purchases
  • Instant/online payments via web and mobile apps
  • Anti-money laundering
  • Rogue stock trading that can manipulate stock markets
  • Identity theft
  • Online account registration
  • Insurance claims