Fraud detection is a significant challenge across various industries, particularly in finance, insurance, and e-commerce. It involves identifying fraudulent activities or transactions, often within vast amounts of data, to prevent financial losses and protect consumers. Fraud detection is part of the bigger problem that every organization needs to tackle which is called preventing digital crime. This can be different attacks related to financial organization backend systems to gain their control or using someone else's credentials to steal money from their accounts. Attack vectors can be different, but all of them focus on stealing financial assets or to gain some valuable information which can be beneficial for the fraudster.
Challenge
The main challenges in fraud detection today are:
- Fraudulent transactions cost the banking industry large amounts of money every year. Every time a bank is attacked and loses some money, it damages its reputation once this becomes known in the public sphere. This makes future losses higher as end customers may decide to change their banks to find a more reliable organization. Also, every bank has to be compliant with regulatory requirements, which also brings a challenge to fight fraud at a very early stage to prevent an event when regulators find mistakes when analyzing provided reports.
- In order to stay on the cutting edge of the fraud prevention techniques, financial organizations are in permanent catch-up with ever evolving fraudster tactics. Banks or other financial organizations can not simply stop evolving their methods to prevent frauds, because fraudsters are constantly developing new methods. Thus, banks have to always invest into their security teams to allow them to invent new methods proactively and be able to respond to new, previously unknown threats.
- In order to make fraud detection effective and respond to the ever increasing speed of new attacks, business users require new systems to control fraud detection systems in real time and be able to do changes instantaneously. In order to tackle fraud detection problems in different countries and be still cost effective in total, the same organization has to design and build a flexible system once to deploy it everywhere. The corporate system has to allow customization to incorporate local properties in a region / country.
Solution
In order to tackle all the challenges mentioned above organizations should implement efficient algorithms and leverage powerful computing infrastructure to ensure real-time processing capabilities. Resulting system in some Risk Engine should continuously monitor and update internal ML models with new data, and leveraging adaptive learning techniques. The built system should be able to control its rules and behavior dynamically, i.e. without requiring a downtime to apply new changes. Organizations should Implement a unified fraud detection system that can analyze data from multiple channels and identify suspicious patterns.
One possible solution is to use Ververica (Unified) Streaming Data Platform and build a VERA-powered Fraud Detection engine which would use streaming data coming directly from the transaction producers and different other sources to enrich raw data before it gets used by an ML model or rules engine to rate a transaction as fraudulent or not.
Generic Fraud Detection can be summarized to the following important steps:
- Define own rules (or train a model) to be able to classify financial transactions as risky / fraudulent
- Solution can involve ML model inference to rate current payment transaction / event
- Role of VERA:
- One of the core component within the larger Risk Engine application which prepares, aggregates and cleans data using massive scale parallel processing
- Local state in the stream processing operator allows to score current event very fast
- Low-level API, SQL API, CEP library are rich tools to implement any complex logic to fight with frauds.
Outcome
Using VERA as Stream Processing Framework, organizations can discover that they can support a variety of previously unknown use cases, so that the risk engine becomes a central system in the organization to prevent digital crimes. The resulting system catches risky transactions in real-time, doing that in batch or with historical data would neglect the benefits of the risk engine and would be too late, as fraud could have already happened.
The ability of the resulting system to change business rules without downtime allows it to prevent any ongoing malicious activity and react in real time. All of that became possible by embarrassing stream processing technologies like Flink and by using data streams to publish new rules and changes into the system. Machine learning models are updated in real-time and can be used by Flink in distributed mode without a need to deploy ML Models as a separate HTTP service.
Fraud Detection with Ververica’s Streamhouse for Real-Time Protection
Introduction: Fraud detection is a critical issue for industries such as finance, insurance, and e-commerce. Detecting fraudulent activities within vast data sets requires powerful real-time processing to prevent financial losses and ensure regulatory compliance. As fraud tactics evolve, organizations must constantly adapt their systems to stay ahead.
The Challenge: Fraudsters continuously refine their methods, making it essential for businesses to detect and mitigate fraud as it occurs. Financial institutions, in particular, face significant risks, including financial loss, reputation damage, and regulatory non-compliance. The challenge lies in designing scalable, flexible fraud detection systems capable of real-time processing and adaptive learning across multiple regions and channels.
Why Ververica? Ververica’s Streamhouse offers the real-time, scalable infrastructure needed for fraud detection. With its ability to process streaming data directly from transaction producers and other sources, Ververica enables rapid identification of suspicious patterns and behaviors. Machine learning models can be updated continuously to adapt to new fraud tactics, and rules can be dynamically changed without system downtime.
Key Benefits:
- Real-Time Fraud Detection: Detect suspicious transactions as they happen, reducing financial losses and improving customer protection.
- Adaptive Machine Learning Models: Continuously update fraud detection algorithms with the latest data, ensuring your systems stay ahead of evolving threats.
- Scalability Across Regions: Build a single, unified system that can be customized for specific countries or regions, improving operational efficiency and regulatory compliance.
- Dynamic Rule Management: Modify fraud detection rules instantly without requiring system downtime, ensuring continuous protection.
- Multi-Channel Analysis: Analyze data from multiple sources simultaneously, enriching transaction data with contextual information for better fraud detection.
What Fraud Detection Systems Should Implement Using Ververica:
- Build real-time streaming pipelines to process transactions as they occur, flagging suspicious patterns instantly.
- Continuously train machine learning models to detect emerging fraud tactics, leveraging adaptive learning.
- Implement scalable fraud detection solutions that can be deployed globally while still allowing for regional customization.
- Enable dynamic rule changes to respond to new fraud trends without affecting system availability.
Ververica’s Streamhouse delivers a robust, real-time solution for detecting and mitigating fraud, helping businesses reduce risk, comply with regulations, and protect their reputation.
Fraud Detection with Ververica’s Streamhouse
Ververica’s Streamhouse empowers businesses with real-time fraud detection by processing streaming data directly from multiple sources and dynamically updating fraud detection models. By leveraging adaptive machine learning and multi-channel analysis, Ververica helps businesses detect suspicious patterns instantly and prevent losses.
Key Benefits:
- Real-time fraud detection across industries.
- Adaptive learning to keep up with evolving fraud tactics.
- Scalability for global deployment with regional customization.
- Dynamic rule management for continuous fraud prevention.
Ververica ensures businesses stay ahead of fraudsters while maintaining compliance and protecting their financial assets.