Systems | Development | Analytics | API | Testing


Real-time Fraud Detection - Use Case Implementation

When it comes to fraud detection in financial services, streaming data with Confluent enables you to build the right intelligence-as early as possible-for precise and predictive responses. Learn how Confluent's event-driven architecture and streaming pipelines deliver a continuous flow of data, aggregated from wherever it resides in your enterprise, to whichever application or team needs to see it. Enrich each interaction, each transaction, and each anomaly with real-time context so your fraud detection systems have the intelligence to get ahead.

5 Ways to Use Log Analytics and Telemetry Data for Fraud Prevention

As fraud continues to grow in prevalence, SecOps teams are increasingly investing in fraud prevention capabilities to protect themselves and their customers. One approach that’s proved reliable is the use of log analytics and telemetry data for fraud prevention. By collecting and analyzing data from various sources, including server logs, network traffic, and user behavior, enterprise SecOps teams can identify patterns and anomalies in real time that may indicate fraudulent activity.

How to Prevent Authorized Push Payment Fraud with Mobile App Testing

This recent article from Finextra regarding the UK payments watchdog consulting on new authorized push payment (APP) fraud reporting rules has me thinking. How can we prevent fraud and protect consumers – and ourselves from scams? APP fraud happens when fraudsters deceive consumers or individuals into sending a payment under false pretenses to a bank account controlled by the fraudster. Real-time payment schemes are irrevocable, so victims cannot reverse a payment once sent.

Fraud Detection with Cloudera Stream Processing

This video shows how Cloudera DataFlow powered by Apache NiFi solves the first-mile problem by making it easy and efficient to acquire, transform, and move data so that we can enable streaming analytics use cases with very little effort. It will also briefly discuss the advantages of running this flow in a cloud-native Kubernetes deployment of Cloudera DataFlow. Then, we will explore how we can run real-time streaming analytics using Apache Flink, and we will use Cloudera SQL Stream Builder GUI to easily create streaming jobs using only SQL language (no Java/Scala coding required).

Fraud Detection With Cloudera Stream Processing Part 2: Real-Time Streaming Analytics

In part 1 of this blog we discussed how Cloudera DataFlow for the Public Cloud (CDF-PC), the universal data distribution service powered by Apache NiFi, can make it easy to acquire data from wherever it originates and move it efficiently to make it available to other applications in a streaming fashion.

Fraud Detection with Cloudera Stream Processing Part 1

In a previous blog of this series, Turning Streams Into Data Products, we talked about the increased need for reducing the latency between data generation/ingestion and producing analytical results and insights from this data. We discussed how Cloudera Stream Processing (CSP) with Apache Kafka and Apache Flink could be used to process this data in real time and at scale. In this blog we will show a real example of how that is done, looking at how we can use CSP to perform real-time fraud detection.

Making the World a Better Place with Data

Much of the hype around big data and analytics focuses on business value and bottom-line impacts. Those are enormously important in the private and public sectors alike. But for government agencies, there is a greater mission: improving people’s lives. Data makes the most ambitious and even idealistic goals—like making the world a better place—possible.

Fraud Processing With Cloudera Stream Processing

SQL Stream Builder, part of Cloudera Stream Processing offering, allows developers and analysts to write streaming applications using industry-standard SQL. In this video, you will learn the interactive experience with syntax checking, error reporting, schema detection, query creation, and creating outputs on fraud detection with its powerful interface and APIs.

Cloudera and NVIDIA Help IRS Fight Fraud, Safeguard Taxpayers

Across the federal government, agencies are struggling to identify, organize, analyze, and act on troves of data. It’s a problem that leaders are working actively to tackle, but they’re in a race against immeasurable volumes of data that is continuously being generated in perpetuity in stores known and unknown. At the Internal Revenue Service, decades’ worth of data exceeds even the most cutting-edge processing capabilities.