The Need for CEP and Streaming Data Analytics Solution
Global connections of 5G are projected to double, aligning with the doubling trend in mobile data usage, which occurs approximately every year. By the end of 2028, nearly 60 percent of cellular IoT connections will be classified as broadband IoT connections, forecasted by Ericsson.
The surge of data in the 5G and IoT era brings about numerous opportunities, but it also introduces new fraud risks and challenges. Employing sophisticated tools and leveraging technologies like Complex Event Processing (CEP) and Streaming Data Analytics, becomes vital to detect and prevent fraudulent activities and enable real-time intelligent decision-making as events unfold, ensuring a proactive approach to combating fraud.
To make data-driven decisions and drive operations in real-time, the adoption of CEP and Streaming Data Analytics becomes essential. These solutions involve capturing, processing, and analyzing streams of data and events in real-time. By seamlessly integrating data from multiple systems, including Business Support Systems (BSS) and Operational Support Systems (OSS), CEP and Stream Data Analytics solutions enable businesses to gain a holistic view of their operations.
Fraud Risk Detection: The Early Stage Advantage
Detecting fraud risks early on is vital for minimizing financial losses and protecting businesses from reputational harm. Given the vast scale and high-speed capabilities of 5G and the Internet of Things (IoT), it is crucial to prioritize early detection and prevention in the fight against fraud, as they prove to be more effective than reconciling issues after they have occurred.
Traditional batch processing methods often fall short in this regard, as fraudulent activities are usually identified after the damage has been done. CEP and Streaming Data Analytics tools play a critical role in addressing this challenge. In general, leveraging their abilities of real-time monitoring, CEP and Streaming Data Analytics tools can identify patterns and detect anomalies that indicate potential fraud risks. Unlike batch processing, these solutions help businesses to proactively respond to emerging threats, taking immediate action to mitigate their impact..
The core difference between CEP and Streaming Data Analytics in detecting fraud at an early stage lies in their approach and emphasis on different aspects of data analysis. CEP focuses on event correlation, real-time rule-based detection, and context awareness, while Streaming Data Analytics leverages continuous data processing using frameworks like Kafka, analytical techniques, machine learning models, and continuous learning to detect fraud at an early stage.
CEP enables businesses to define rules and patterns that represent fraudulent behavior. These rules can be continuously applied to real-time monitoring event streams, facilitating the detection of emerging fraud patterns. CEP incorporates machine learning algorithms and advanced analytics techniques to adapt to evolving fraud tactics. By combining historical data with the event real-time monitoring, CEP can identify previously unseen fraud patterns, reducing the risks of false negatives and false positives. Suspicious events can trigger immediate actions, such as blocking transactions, notifying stakeholders, or initiating investigations.
Streaming Analytics is a form of analytics that can continuously ingest, process and analyze real-time streaming data. Data can come from various real-time sources perpetually. This enables businesses to take immediate action while events are still happening. Streaming analytics platforms can gather and analyze large volumes of data arriving in “streams” from always-on sources. By extracting relevant features from the data streams, Streaming Analytics captures important characteristics of potentially fraudulent activities.
Neural Technologies offers advanced solutions for fraud risk management, including our cutting-edge Complex Event Processing (CEP) solution and Streaming Data Analytics solution. These solutions, as part of our powerful Data Integration suite, incorporate machine learning (ML) techniques to equip businesses with advanced capabilities to detect and mitigate fraud in the early stage and near real-time, empowering them to safeguard their financial health and reputation.
Early detection of fraud risks allows for timely intervention and investigation. By identifying suspicious activities at their inception, businesses can initiate internal investigations, collaborate with law enforcement agencies, and gather crucial evidence to prosecute fraudsters. This proactive approach not only minimizes financial losses but also serves as a strong deterrent, sending a clear message that fraud will not be tolerated.
The Future of CEP and Streaming Data Analytics
Looking ahead, the future of CEP and Streaming Data Analytics solutions holds tremendous potential across sectors. As technology advances, the combination of these two solutions will unlock even more powerful event-processing capabilities.
The IT community, including data analytics teams, application architects, process modelers, and project leaders, needs to recognize the commercial viability of stream analytics in new application areas. Conducting proof-of-concept work will be crucial in showcasing the benefits and opportunities.Industries like the Internet of Things (IoT), eCommerce, and customer engagement are already leveraging CEP and Streaming Data Analytics solutions to drive significant improvements in event processing effectiveness and speed.
As 5G and IoT-related technology continue to advance, CEP and Streaming Data Analytics solutions will play an increasingly critical role in managing the vast amount of data generated. Neural Technologies is committed to staying ahead of the curve, continuously enhancing our solutions and unleashing the full potential of CEP and streaming data analytics.
Features of our integrated, end-to-end Complex Event Processing Solution:
- ETL and ELT Data Integration: Support structured, unstructured, semi-structured and raw data types.
- Big Data Integration: Capture, curate, analyze, search, store, transfer, and present complex data in a simple, user-friendly way.
- Connector by Configuration: Vendor independent to formats/ external systems through configuration of integration.
- Business Intelligence and Analytics: Enhanced data analytics including Machine Learning/ AI with integrated storage support to Big Data, RDBMS, NoSQL.
- Real-time Streaming Gateway: Online and offline large scale data streaming, collection and transformation, and distribution such as Kafka (Avro), MQSeries, RabbitMQ, MQTT for IoT, UDP.
Features of our time to value Streaming Data Analytics Solution
- Data Pipelines: Act as Consumer and/or Producer.
- Real-Time Streaming Gateway: Integrate real-time streaming to other systems and applications through open APIs.
- Data Processors: Extensive data transformation, aggregation, filtering, splitting, assembly tools in-built.
- AI/ML Data Driven: Neural Technologies ActivML provides advanced machine learning and AI tools as part of the streaming data analytics toolkit.
- Big Data Integration: Capture, curate, analyze, search, store, transfer, and present complex data in a simple, user-friendly way.
- Business Intelligence and Analytics: Execute business intelligence and analytics through results of event message processing.
- Avro Repository: Direct integration to Avro for shared schemas.
- Analytics Scripting Integration: Configurable scripting supported with Python and Java. Extensive data transformation in-built.
- Kafka Cluster Manager: Cluster and security management. Monitoring and visualization of message queues.
With the capabilities of CEP and Streaming Data Analytics, like real-time monitoring, pattern detection, and immediate action capabilities, businesses can proactively identify and respond to fraud risks. As technology progresses, the future of the solutions holds great promise across various sectors, revolutionizing operations, enhancing efficiency, and driving innovation in the digital landscape.
