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Fraud

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).
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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.

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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.

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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.
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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.

Fraud Processing with SQL Stream Builder

SQL Stream Builder 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.
unravel

Mastercard Reduces MTTR and Improves Query Processing with Unravel Data

Mastercard is one of the world’s top payment processing platforms, with more than 700 million cards in use worldwide. In the US, nearly 40% of American adults hold a Mastercard-branded card. And the company is going from strength to strength; despite a dip in valuation of more than a third when the pandemic hit, the company has doubled in value three times in the last nine years, recently reaching a market capitalization of more than $350B dollars.

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How can DataOps improve your financial institution's fraud program and mitigate risks?

Fraud comes in different forms, from client-facing credit card fraud to internal fraudsters twisting the loan portfolio. Banks (and other financial institutions) need to stay vigilant and act fast to prevent the loss of both money and reputation that follows each fraudulent incident. Fraud is expensive, but fraud prevention, detection and remediation can also be costly.